pure.au.dkpure.au.dk/portal/files/8784/...effects_on_economy_-_an… · Web viewThe monetary policy and its effects on economy - an European view. Academic Advisor: Jan Bartholdy

“The monetary policy and its effects on economy - an European view”MSc. Finance and International Business Nicoleta Cristina Alexandru

The monetary policy and its effects on economy - an European view

Academic Advisor: Jan Bartholdy

This paper takes a closer look about how the European Central Bank is conducting the monetary policy in Europe, what are the goals, the instruments, the transmission mechanism and the results. The first chapter makes a theoretical review of what monetary policy means for the Euro Area and the member states. The second chapter studies the concept of financial crises; given the current disruption in the world economy and the past experiences, the review puts into perspective the possible solutions that can be employed in order to avoid a general collapse. The third chapter contains an empirical study with regards to the effect that European Central Bank’s monetary policy measures have in the Euro Area, both for the key euro area indicators and for the non-financial companies.

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Chapter 1. The monetary policy of the European Central bank

IntroductionFounded on 30 June 1998 in Frankfurt, the European Central Bank has the responsibility of

leading a single monetary policy in the Euro Area, as people in 16 European countries have euro as

their currency. Starting 1 January 1999, its main tasks have been to maintain price stability in the

Eurozone and to implement the European monetary policy defined by the European System of

Central Banks (ESCB). The Executive Board and Governing Council administer the European System of

Central Banks (ESCB), whose roles are to manage money supply, conduct exchange operations, hold

and manage the official foreign reserve assets of the Member States and ensure the smooth

functioning of payment systems1.

The EC Treaty delegates the ECB and the National Central Banks, associated in the form of

Eurosystem, with a clear mandate and a primary objective of maintaining the price stability in the

euro area, i.e. preserving the purchasing power of the euro. The achievement of this monetary policy

objective is widely proven through economic theory and empirical research to significantly contribute

to sustainable growth, economic welfare and job creation.

Basic notions2

At a basic, theoretic level, inflation is defined as a general increase in the price of goods and

services, in a certain time period and region, leading to a decline in the value of money and their

purchasing power; at the same time, deflation is defined oppositely, as a fall in the overall price level

over a certain time span and region. Economic evidence, for a wide variety of countries and periods,

shows that, in the long run, economies with lower inflation appear on average to grow more rapidly

in real terms, as the erosion in the purchasing power of money means a loss of real value in the

internal medium of exchange and unit of account in the economy.

On the other hand, episodes of deflation have often been associated with the supply of

goods going up (due to increased productivity) without an increase in the supply of money, or the

demand for goods going down combined with a decrease in the money supply. The phenomenon of

deflation is particularly important to be avoided, given that it implies nominal interest rates to fall

below zero, making the lending activity impossible (as the public would prefer to hold cash than to

lend or make deposits at a negative rate). In this case, any monetary policy measure taken by the

1 Europa Glossary, European Central Bank (http://europa.eu/scadplus/glossary/european_central_bank_en.htm)2 Dieter Gerdesmeier „Price stability: why is it important for you?” European Central Bank, 2009

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central bank would not be able to sufficiently stimulate the aggregate demand through the interest

rate instrument.

A situation of price stability is met if, on average, there is no change in the general price level.

Still, frequent movement in the prices of certain good and services are quite normal in market-based

economies, as a consequence of technological progress, but in a situation where falls and rises in

prices offset each other, the price stability is still maintained. Among the numerous advantages of

price stability, one can find reduced uncertainty about price levels and improved transparency of

relative prices, reduced inflation risk premia in interest rate, avoidance of unnecessary hedging

activities, reduced distortionary effects of tax and social security systems, increased benefits of

holding cash, prevention of arbitrary distribution of wealth and income, and overall financial stability.

For obvious reasons, price stability and inflation also make subject of one of the convergence

criteria that must be met by each Member State before it can adopt the euro as part of the third

state of the Economic and Monetary Union - the average inflation rate of the candidate member

should not exceed by more than one and a half percentage points that of the three best performing

Member States in terms of price stability on a time period of one year before the examination.

The transmission mechanism through which the actions of the European Central Bank are

transmitted through the economy and ultimately to prices is extremely complex and moreover,

variable over time. Still, its basic features are clear: as the central bank is the monopolistic issuer of

the bank notes and bank reserve, i.e. the so-called “monetary base”, therefore it is able to influence

market conditions and short-term interest rates.

In the short run, a change in money market short term interest rates, all things being equal,

has an impact on spending and saving decisions of the companies and households, and may also

affect the supply of credit. This is mainly possible as policy rates expectations, e.g. the short-term

interest rates on loans given to the banks, translate into a wide range of long-term bank and market

interest rates. Higher interest will determine households to increase savings as the return in terms of

future consumption is higher. At the same time, companies will diminish their investments, as fewer

of them will bring a return high enough to compensate the increased cost of capital.

Still, this process implies a certain time lag, as it usually takes month for companies to set up

an investment plan, especially for high valued items like industrial plants or high-tech equipment,

and, also, many consumers will not change their consuming habits immediately, following

movements in interest rates. In conclusion, a monetary policy measure cannot influence economy

(e.g. the overall demand for goods and services) in the short run.

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The ECB’s Monetary Policy

The idea of a economic monetary union did not appeared at the very first beginning of the

European Economic Community in the ‘50s, as the initial idea was to form a customs union and a

common agricultural market. Still, the need for a monetary identity was brought in the 1960s, as the

international environment and the differences in the European countries’ policy priorities threatened

the functioning of the simple union. However, the attempt was not successful, under the pressure of

divergent policy responses to the economic shocks of the period and the so-called “snake” (a stable

fluctuation margin of ±2.25% around each currency’s central rate vis-à-vis the US dollar) was

suspended and the rates fluctuated freely.

A straightforward decision to the integration process came into the European landscape in

June 1988, when the objective to gradually achieve economic and monetary union was reconfirmed

by the European Council. The president of the European Commission at that time, Jaques Delors,

came with a three step plan for the introduction of an Economic and Monetary Union. The first step,

launched in 1990, was aiming at reducing the disparities between the economic policies of the

member states, intensifying the monetary cooperation and removing all obstacles to financial

integration. The second step, beginning 1994, set up the organizational structure of the EMU and

strengthened the economic convergence, while starting 1999, in the last stage, the exchange rates

were locked irrevocably and all the Community institutions and bodies were be assigned their full

monetary and economic responsibilities.

As mentioned before, according to the EC Treaty, the final goal of the ECB’s monetary policy

is price stability. In other words, the European Central Bank must influence the money market

conditions, i.e. the short term interest rates, in such a way that price stability is maintained in the

medium term. Price stability, as it is defined by the EC Treaty, implies a year-on-year increase in the

Harmonised Index of Consumer Prices3 (HICP) of below 2% in the medium term4.

For this to be possible, inflation expectations must be firmly considered and the national

central banks, must, in turn, to elaborate their targets to a systematic and consistent method of

conducting monetary policy. Moreover, the exact mechanism and lagged transmission of any policy

measure must also be taken into consideration, therefore the strategy should always have a medium

term focus, in order to avoid introduction of unnecessary volatility in the economy. However, short-3 The HICP aims to be representative of the developments in the prices of all goods and services available for purchase within the euro area for the purposes of directly satisfying consumer needs. It measures the average change over time in the prices paid by households for a specific, regularly updated basket of consumer goods and services. („Measuring inflation – the Harmonised Index of Consumer Prices (HICP)”, European Central Bank) 4 Article 105 (1) of the EC Treaty. This benchmark is also a safety margin against deflation, as the effectiveness of the policy measures is not fully certain even if they can be carried out in the case of zero nominal interest rates and, as discussed before, the event of deflation is even less desirable and more costly than inflation.

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term volatility in the inflation rate is always possible, e.g. due to changes in international commodity

prices or direct taxes, so no policy measures can offset unanticipated price shocks.

On the other hand if an excessively aggressive policy carried out to restore price stability

within a very short time period could cause a significant cost in terms of output and price volatility

that would have a final effect on price developments in the long run. Moreover, the medium term

orientation gives the ECB a flexibility of reaction in case of economic shocks that might occur.

All in all, a successful monetary policy takes into account a broad base of relevant

information, in order to understand the factors driving economic developments, therefore a small set

of indicators or a single economic model is definitely not reliable.

The transmission mechanism

In order to take the best decision regarding monetary policy measures, ECB makes a

comprehensive analysis based on two complementary perspectives on the determination of price

developments. The first perspective, known as the „economic analysis” implies the assessment of

short to medium-term determinants of price developments with a focus on real activity and financial

conditions in the economy, as in this time span, the determinants are significantly influenced by the

interplay of supply and demand in the goods, services and factor markets.

Among the economic and financial variables that asses the dynamics of real activity and are

subject of the above analysis we can find: aggregate demand and its components, fiscal policy,

capital and labour market conditions, developments in the exchange rate, financial markets, balance

of payments and balance sheet positions of euro area sectors.

The next chart provides an illustration of the main transmission channels of monetary policy

decisions, as it is seen by the European Central Bank.

A illustration of the transmission mechanism from interest rates to prices5

5 As it appears on the European Central Bank website ( http://www.ecb.europa.eu/mopo/intro/transmission/html/index.en.html)

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While price developments in the industrial sector, as measured by producer prices, and

labour costs may have a significant impact on price formation and also provide information on the

competitiveness of the euro area economy, indicators of output and demand provide information on

the cyclical position of the economy. Balance of payments and external trade statistics show the

impact of exports and imports on demand conditions and, moreover, to the exchange rate and

commodity prices.

Movements in asset prices may affect price development via income and wealth effects, as in

case of a e.g. equity price rise, share-owning individuals might chose to increase their consumption,

raising domestic demand and, therefore, inflationary pressures, while those who are planning to take

a loan could find it more easy to obtain, given the increase in the value of the collateral (in this case,

the shares), again with a latter impact on spending and final demand. Moreover, through bond

trading, the financial markets participants reveal their expectations about developments in real

interest rates and inflation expectations, therefore asset markets and asset prices are forward-

looking by nature and can be analysed as such.

Last but not least, developments in the exchange rate have close implications for price

stability, through the influence on import prices and, furthermore, on domestic producer and

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OFFICIAL INTEREST RATES

BANK AND MARKET INTEREST RATES

MONEY CREDIT

ASSET PRICES

EXCHANGE RATE

WAGE AND PRICE SETTING

SUPPLY AND DEMAND IN GOODS AND LABOUR MARKETS

DOMESTIC PRICES

IMPORT PRICES

PRICE DEVELOPMENTS

EXAMPLES OF SHOCKS

OUTSIDE THE CONTROL OF THE CENTRAL

BANK

CHANGES IN GLOBAL

ECONOMY

CHANGES IN FISCAL POLICY

CHANGES IN COMMODITY

PRICES

EXPECTATIONS


consumer prices . Even if the euro area is a relatively closed economy, the evolution of the exchange

rate has another impact on the price competitiveness of the European domestic products on the

international markets.

The models in this paper make use of a small part of the above variables: gdp, gdp deflator,

an index of commodity prices, final demand, inventories, durable and non-durable goods

consumption, residential investment, companies’ investment rate and, from a statement of

comprehensive income point of view, corporate profit and employee benefits.

The second perspective, focused on a long-term horizon, is known as the „monetary analysis”

and it attaches a more prominent role to monetary and credit developments. This analysis studies

the long-run link between money and prices, being more a means of cross-checking the

short/medium-term indicators of the first “economic analysis” from a long/medium-term

perspective.

More precisely, the ECB takes into consideration the monetary aggregate M3 6, as empirical

evidence confirms this aggregate to have the properties of a stable money demand, being a leading

indicator for future price developments in the euro area. Given that, it is considered that an annual

growth rate of M3 of 4,5%7 is considered to be compatible with the price stability over the medium

term. Still, this method is only used to analyse and asses the information content of monetary

developments in the euro area, as no evidence support the existence of a direct link between short-

term monetary developments and monetary policy decisions, therefore if M3 deviates significantly

from the 4,5% benchmark, the ECB does not react mechanically.

The rationale behind this behaviour is given by the existence of so-called special factors, such

as institutional changes. Modifications in tax treatment of interest income or capital gains shifts the

private sector’s preference for money holding (deposits vs. alternative financial instruments),

affecting the development of M3 without being necessarily important for the long term evolution of

prices.

6 According to the ECB definitions, the M1 monetary aggregate includes items with so called immediate or zero liquidity as banknotes, coins and other instruments than can be immediately converted into currency or used for cashless payments, as e.g. overnight deposits. M2 includes M1 plus deposits with a maturity of maximum 2 years or redeemable at a period of notice of up to three months - these can be converted into liquid money, but with restrictions such as in advance notification, penalties or fees. Last, M3 contains M2 plus some marketable instruments issued by the private monetary financial institutions: repurchase agreements, money market fund shares/units and debt securities with a maturity of up to two years (including money market paper).7 The econometrical framework of this decision is based on the “quantity equation” of money, ΔM = ΔYR + ΔP – ΔV, which states that change in money equals the change in the nominal transaction of an economy, approximated by the change in the real gdp plus the change in inflation, minus the change in the velocity of money.

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The two pillar analysis provides a cross-check of the conclusions that appear from the short-

term economic analysis, to make sure all the relevant information regarding price developments is

taken into consideration, as both asses different risks to price stability.

The combined approach reduces the risk of policy errors cause by the reliance on a single

indicator, model or forecast, given that a diversified approach helps carrying a robust monetary

policy in an uncertain environment.

Monetary policy instruments8

The operational framework through which the monetary policy is carried out within the

Eurosystem consists of three categories of instruments: open market operations (of which the main

refinancing operation interest rate instrument is closely studied in this paper, both theoretical and

empirical), standing facilities and minimum reserves requirements for credit institutions.

Open market operations

The open market operations are used to manage the liquidity situation on the market and,

conducting the liquidity rates and signalling the direction of the monetary policy. According to their

scope, regularity and procedure, the operation can be divided into four categories: main refinancing

operations, longer-term refinancing operations, fine-tuning operations and structural operations -

each with its specific instruments.

The main open market instrument of the Eurosystem consists of reverse transactions and this

instrument can be employed in all the above categories of operations. The reverse transactions are

operation through which the Eurosystem buys or sells eligible assets under repurchase agreements

or conducts credit operations against eligible assets as collateral.

The main refinancing operations (MRO) consist of regular liquidity-providing reverse

transactions with frequency and maturity of one week, carried out by the National Central Banks,

that normally provide the majority of refinancing to the financial sector, therefore being the most

important open market operations conducted by the Eurosystem.

As a response to the severe overbidding developed in the fixed rate tender procedure, the

Governing Council of the ECB has decided to switch to variable rate tenders in June 2000, therefore,

the minimum announced bid rate was supposed to take over the role played until then by the rate in

fixed rate tenders9. 8 General Documentation „The Implementation Of Monetary Policy In The Euro Area” November 2008, © European Central Bank9 In a variable rate tender, counterparties bid the amounts of money and the interest rates at which they want to enter into transactions with the national central banks.

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The procedure was changed back to fixed rate tender procedure in October 2008 as an

attempt to steer liquidity towards balance conditions, given the international economic environment

and the financial crisis, but, at the same time, to comply with the price stability objective. The

modification is meant to remain in place as long as needed.

The MRO interest rate was decreased gradually since, until the level of 1% (with effect from

May 2009), reflecting the market weakening of economic activity in the euro area and globally. ECB’s

estimations regarding both global and euro area demand showed a very weak development over

2009, but a gradual recovering in the course of 2010. This decision also complies with the Governing

Council’s scope of lowering inflation rates, corresponding the 2% level over the medium term.

The actual strategy was proven efficient as the latest information confirms an improvement

of the economic activity in the second half of this year. Inflation is also on the chart on the medium

term as money and credit growth is slowing down leading to low inflationary pressures.

However, the gradual economic recovery forecasted for 2010 remains highly uncertain.

Positive and stronger than anticipated effects might occur from the extensive macroeconomic

stimulus and other policy measures taken with a side effect on overall confidence, labour market and

foreign demand. On the other hand, we can find a stronger than projected negative feedback loop

between the real economy and the financial sector, further increases in oil and commodity prices,

protectionist pressures at a national level or uncoordinated corrections of global imbalances.

The longer-term financing operations are as well reverse transactions conducted by NCBs

that provide liquidity and usually have monthly frequency and 3-months maturity; irregular

frequencies and other maturities are also possible. Their scope is to provide counterparties with

additional longer-term refinancing and are not intended to send signals to the market regarding

developments in the Eurosystem’s monetary policy.

Fine-tuning operations are carried out on an ad hoc basis in case of unexpected liquidity

fluctuations in the market in order to smooth the effects on the interest rates. They are usually

executed as reverse transactions, but can also take form of outright transactions10, foreign exchange

swaps and collection of fixed term deposits11.

Structural operations occur whenever the ECB wants to adjust the structural position of the

Eurosystem vis-à-vis the financial sector. This can be carried out in form of reverse transactions and

10 An outright transaction implies a full transfer of ownership of an eligible asset from the seller to the buyer with no connected reverse transfer of ownership.11 The Eurosystem may invite counterparties to place remunerated fixed-term deposits with the national central bank in the Member State in which the counterparty is established.

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issuance of debt certificates (executed by NCBs through standard tenders), plus outright transactions

(executed by NCBs through bilateral procedures).

Standing facilities

The standing facilities help providing or absorbing overnight liquidity, signal the general

stance of monetary policy and bound overnight market interest rates. Using the marginal lending

facility, the counterparties can obtain overnight liquidity from the corresponding national central

banks against eligible assets. At the same time, the counterparties can use the deposit facility to

make overnight deposits with the national central banks.

Minimum reserves requirements

All the credit institutions must hold minimum reserves on accounts with the national central

banks in compliance with the Euroystem’s minimum reserve system.

The scope of minimum reserves system is to follow the establishment of the money market

interest rates, by giving institutions an incentive to smooth the effects of temporary liquidity

fluctuations. Moreover, they create or even enlarge a structural liquidity shortage and control in a

certain measure the monetary expansion and this might be helpful in improving the ability of the

Eurosystem to operate efficiently as a supplier of liquidity.

Chapter 2. Financial Crises Around the World

Financial Crises in Theory

How the financial crises appear and what is the best to do to solve them are two questions

that economists around the world had many opportunities to answer to, especially since 2007. While

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the number of theories regarding the financial crises development and prevention is significant, still

little consensus exists and financial crises are a regular occurrence around the world.

To start at the beginning, what is a financial crisis? The term applies in a various types of

situations when financial institutions or assets meet a sudden loss of their value. Many of them have

been associated with bank panics (or bank runs), usually causing recessions. Other cases known as

‘financial crises’ include asset market crashes, currency crises and sovereign defaults.

Banking crises appear when one or more banks meet a situation where a significant number

of depositors withdraw their money as a consequence of rumours that the bank is or might become

insolvent. The phenomenon rapidly becomes a self fulfilling prophecy causing a chain of bankruptcies

and, in the end, a long economic recession.

Speculative bubbles appear when the price of a financial asset exceeds the present value of

its future income until maturity and investors buy that specific asset in order to sell it later at a higher

price instead of buying it for its future generated income. As increased demand leads to price

increases, the investors will buy as long as they forecast others to buy. However, at a certain moment

in time, many will decide to sell and the price will fall, causing a crash.

A currency crisis (also known as a ‘balance of payments crisis’) occurs when the value of a

currency drops quickly, undermining its ability to serve as a medium of exchange or a store of value,

usually accompanied by speculative attacks. Public authorities (i.e. central banks) often counter such

attack using the country’s own or foreign currency reserves to satisfy the excess demand for a given

currency.

When nations have unpredictable inflation or unstable exchange rates, they are forced to

issue bonds denominated in more stable foreign currencies and with higher yield due to the

increased probability of default. A sovereign default appears when such a nation fails to buy the

necessary amount of foreign currency at bond’s maturity time.

According to an IMF working paper12, banking crises were most frequent during the early

1990’s, with a maximum of 13 systemic banking crises starting in the year 1995. At the same time,

the early 1980’s represented a high mark for currency crises, with a peak in 1981 of 45 episodes.

Sovereign debt crises were also relatively common during the early 1980’s, with a peak of 10 debt

crises in 1983. Over the period of 1970 to 2007, there were 124 banking crises, 208 currency crises,

and 63 sovereign debt crises, given that several countries experienced multiple crises: of the 124

12 Luc Laeven and Fabian Valencia, „Systemic Banking Crises: A New Database”, IMF Working Paper, WP/08/22411


banking crises, 42 are considered twin crises13 (bank and currency crises) and 10 can be classified as

triple crises14 (bank, currency and debt crises).

In the Keynesian view, the main symptom of a financial crisis is a so-called “liquidity trap,” in

which people prefer to hold cash instead of investing in capital goods. This behaviour is actually

considered to be a psychological phenomenon, therefore inexplicable. Mason Gaffney (2009) shows

that the loss of liquidity actually has a real basis. As the key element in creating liquidity is the

monetization of various types of collateral, if this collateral turns over slowly, banks lose liquidity and

the solution is to restore the principles of the “real bills” doctrine that requires loans to be self-

liquidating.

Shigenori Shiratsuka (2003)15 consider extremely important to correctly asses weather

structural changes occur or it is a process of entering in a “new economy”. More precisely, if the

productivity is rising, marking a change in the economic structure, a misinterpretation leading to

monetary tightening would constrain economic growth potential. On the other hand, a bubble might

be mistaken as a transitional process to a ‘new economy,’ and the central bank allows inflation to

ignite.

Honohan and Laeven (2003) and Hoelscher and Quintyn (2003) differentiate between two

phases in a financial crisis, meaning that different types of measures should be taken into

consideration for each of them.

In the containment phase16, the financial crisis is still developing, therefore the governments

should implement policies to restore the public confidence in the financial system in order to

minimize the repercussions on the real sector. The resolution phase implies concrete operational and

financial restructuring measures aimed at corporations and financial institutions. However, bad

short-term containment policies, reduce the potential success of the long-term resolution measures,

as they are somehow integrated within.

Immediate policy measures include17:

13 In IMF’s view, a twin crisis in year t is considered to be as such if it complies with the condition of a banking crisis in year t, combined with a currency crisis during the period [t-1, t+1].14 Accordingly, a triple crisis in year t is considered to be as such if it complies with the condition of a banking crisis in year t, combined with a currency crisis during the period [t-1, t+1] and a sovereign debt crisis during the period [t-1, t+1].15 Shiratsuka, Shigenori (December 2003). ”Asset Price Bubble in Japan in the 1980s: Lessons for Financial and Macroeconomic Stability”, Institute for Monetary and Economic Studies, Bank of Japan, IMES Discussion Paper Series 2003-E-1516 Luc Laeven and Fabian Valencia, „Systemic Banking Crises: A New Database”, IMF Working Paper, WP/08/22417 idem

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(a) suspension of convertibility of deposits, which prevents bank depositors from seeking

repayment from banks,

(b) regulatory capital forbearance, which allows banks to avoid the cost of regulatory

compliance (for example by allowing banks to overstate their equity capital in order to avoid the

costs of contractions in loan supply),

(c) emergency liquidity support to banks,

(d) a government guarantee of depositors.

In case disruption of banking is part of a wider financial and macroeconomic turbulence,

regulatory forbearance on capital and liquid reserve requirements may prove to be the most

appropriate in these conditions (Laeven and Valencia, 2008).

The second phase policies aim for restoring the normal functioning of the credit and legal

systems, and the rebuilding of banks’ and borrowers’ balance sheets, as economic growth cannot

restart until productive assets and banking franchises are in control and correspondingly used by

solvent private entities.

The long-term policies corresponding to the second phase of a recent crisis include18:

(a) conditional government-subsidized, but decentralized, workouts of distressed loans;

(b) debt forgiveness;

(c) the establishment of a government-owned asset management company to buy and

resolve distressed loans;

(d) government-assisted sales of financial institutions to new owners, typically foreign;

(e) government-assisted recapitalization of financial institutions through injection of funds.

According to Calomiris, Klingebiel and Laeven, (2003), countries typically apply a combination

of resolution strategies, including both government managed programs and market-based

mechanisms. Marked-based programs come to complete the Government’s actions by strengthening

the capital base of financial institutions and borrowers in order to allow them to renegotiate debt

and obtain new loans. These mechanisms resolve the coordination problems that appear in case of

massive debtor and creditor insolvencies having rather low direct and indirect costs, particularly if

they achieve the desirable objective of selectivity (e.g. focusing public resources on companies and

banks that are worth receiving the rescue package).

18 idem13


The 20th Century’s Financial Crises

One of the first notable crisis of the 20th century was the Amazon rubber boom in 1910. After

two year of gradual increase in rubber price, the industrial world caught a rubber fever and investors

all over the world started to bet on raw rubber production given the unprecedented rise in rubber

value at the beginning of January 1910.

The wonder lasted only 5 months. The situation was not bringing concern at that moment, as

a decrease in prices was naturally expected, but by November 1910 the prices had fallen from $3 to

below $1,2. This was the beginning of a decade long decline that left the Amazon’s extracting

economy wrecked, and made room for the Asian plantations.

The Great Depression is, so far, the largest and most important economic depression of the

20th century, being the longest, most widespread, and deepest depression of the 20th century, and

an example for the 21st century of how far the world's economy can decline. It originated in the US,

with the stock market crash from October 29, 1929 (also known as the “black Tuesday”) and spread

rapidly to almost all the countries in the world, for almost a decade. Personal income, tax revenue,

profits, prices, international trade, all dropped, while unemployment reached alarming rates. The

most affected areas were those depending on the primary sector, as cropping, mining and logging,

but construction and heavy industry were also seriously hit19.

In the monetarist view, what started like a normal recession quickly turned into a great

depression because of FED’s lack of response to the banks’ collapse. By allowing some large public

banks to go down, panic and widespread runs on local banks with a disastrous effect on the money

supply. Without significantly less money, businessmen were not able get new loans or to get their old

loans renewed, thus stopping the investments.

On the other hand, Fisher argued20 that the main cause leading to the Great Depression was

over-indebtedness and deflation, as loose credit fuelled speculation and asset bubbles. In the

Keynesian view21, the low aggregate expenditures in the economy contributed to a massive decline in

income and to employment that was well below the average. Keynesian economists lobby for

government higher involvement in time of crises through increasing government spending and/or tax

cutting, as the private sector would not invest enough to bring the economy out from recession.

19 http://www.questia.com/PM.qst?a=o&d=98065455 , Broadus Mitchell (1947), „Depression Decade: From New Era through New Deal, 1929-1941”, Publisher: Rinehart. Place of Publication: New York.20 Fisher, Irving (October 1933). "The Debt-Deflation Theory of Great Depressions". Econometrica 1: 337–35721 Keynes, John (1936). „The General Theory of Employment, Interest and Money”, Publisher „Palgrave Macmillan” (1997 edition).

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http://www.questia.com/PM.qst?a=o&d=98065455


According to the Austrian School view, the expansion of the money supply in the 1920 was

the main cause of a unsustainable credit-driven boom that further became the underlying cause of

the Depression22. This artificial interference of the Government in the economy, in order to sustain

Great Britain’s efforts to return to the gold standard at pre-WW1 parity, was a disaster prior to the

Depression. According to Rothbard, FED’s tightening in 1928 was undue, delayed the market's

natural adjustment and made the road to complete recovery more difficult, given the population’s

loss of confidence in the banking system and the conservative behaviour of the surviving banks.

Livingstone’s view23 is a bit different of the monetarist view in this matter - he thinks that the

shift of income shares away from wages and consumption to corporate profits that produced a tidal

wave of surplus capital. This was not profitably invested in goods production, but in other promising

markets, e.g. securities listed on the stock exchange or real estate. Additional investments in

expansion of the productivity capacity were not needed, as maintaining and replacing the existing

capital stock was enough to enlarge capacity, productivity, and output24. As the demand for

consumer durables was declining (phenomenon observed starting 1926), the nonfinancial companies

decided to pull out their money from the loan market, causing the stock exchange crash, while banks

remained on the balance sheets with the so-called “distressed assets”, i.e. the securities listed on the

stock exchange. Following Livingstone’s analysis, the reverse shift of income shares away from profits

toward wages, which permitted recovery, was determined by government spending and enforced by

labour movements.

Forbes (2009) considers that the Smoot-Hawley Tariff Act of 1929-1930, which imposed

enormous taxes on a broad range of imports, was the main cause that triggered the Depression25.

This started a trade war that finally dried up the world commerce and the capital flows, the economic

situation being worsened by the tax increases adopted by the following governments, deepening the

crisis.

The end of the Great Depression came at different points in time across the globe. While US

economy started to recover around 1933, in other countries the depression lasted for more than a

decade. Massive governmental programs, extensive public policies and the abandonment of the gold

standard are thought to be the main ingredients26. Choudri and Kochin (1980), Eichengreen and 22 Rothbard, Murray (1963). „America's Great Depression”, Ludwig von Mises Institute, 5th edition, 200023 Livingston, James. (May/June 2009). „Their great depression and ours”, Challenge, vol. 52, no. 3, pp. 34–5124 The 1920s was the period when new technology consumer durables became the driving force of economic growth, with spectacular increases in nonfarm labor productivity and industrial output, at the same time with significant decreases in net investments.25 Forbes, Steve (October 2009). „Capitalism: A True Love Story”. Forbes, 00156914, Vol. 184, Issue 726 Parker, Randall. "An Overview of the Great Depression". EH.Net Encyclopedia, edited by Robert Whaples. March 16, 2008. URL http://eh.net/encyclopedia/article/parker.depression

15


Sachs (1985), Temin (1989), and Bernanke and James (1991) discovered that the sooner a country

gave up the gold standard, the sooner its economy started to recover, as departure from the gold

standard kept the countries away from the ravages of deflation.

The political environment changed a lot and many countries embraced left or right views,

while liberal based societies were weakened, giving dictators like Adolf Hitler and Benito Mussolini

the chance to prepare the WW2.

Another notable crisis of the 20th century was the 1973 oil crisis, causing the 1973-1974 stock

market crash, and putting the prices up-high. The oil crisis started when the members of the

Organisation of the Arab Petroleum Exporting Countries decided to initiate an embargo as a response

to the U.S. decision to re-supply the Israeli military forces in the 1973 Arab-Israeli War. The embargo

lasted until March, 1974. At the same time, the OPEC members decided to stabilize their real income

by raising oil prices. As most of the industrialized countries were depending on the OPEC as a

predominant supplier, their economic activity followed significant downturn. As a counter act, the

target countries began a series of actions to reduce their dependency.

However, the main cause of the stock market crash in 1973-1974 is considered to be the

collapse of the Breton Woods system after US unilaterally terminated the convertibility of the dollar

to gold. It affect all the major stock markets, especially UK, given the fall in the property market and

another banking crisis that forced the Bank of England to bail out several lenders27.

Starting 1980s, the Latin American countries faced a long period of economic crises. In the

previous two decades, many of them, most important being Mexico, Brazil and Argentina indebted

themselves for the scope of industrialisation, especially for infrastructure projects. Everything

seemed to work just fine, until the 1970s oil crisis. Developing countries felt a greater need for

liquidity at that moment and international banks continued to finance their foreign debt through the

deposits made by the petroleum exporting countries. However, at the beginning of the 1980s,

international capital markets realised that Latin America countries were not able to pay back the

loans28. In response to the crisis, most of them adopted an export-oriented strategy. Although all the

countries implemented debt management programs in agreement with their international creditors,

the debt levels continue to be high, the Latin America and Caribbean debt in 2004 accounted for

62,3% of total emerging markets debt traded worldwide that year29.

27 Ringshaw, Grant (1 February 2003). "Why we should fear a nasty 70s revival". Daily Telegraph (http://www.telegraph.co.uk/finance/2841497/Why-we-should-fear-a-nasty-70s-revival.html retrieved on 10.11.2009)28 Schaeffer, Robert. Understanding Globalization, p.9029 Emerging Market Trade Association survey, quoted by Wikipedia.

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Just a few years later, on October 19, 1987, financial markets around the world simply

crashed. Starting Hong Kong, it travelled to Europe and US, making the largest one-day percentage

decline in stock market history30. Several explanations were given for this strange event, of which

worth being mentioned are program trading, overvaluation, illiquidity, and market psychology.

Program trading is actually the most popular cause: computers perform rapid stock

executions based on external inputs, such as the price of related securities, attempting to engage in

arbitrage and portfolio insurance strategies, therefore it was extensively used. The risk, however, was

to meet such a market situation that could cause the blindly selling of the stocks as markets fell,

exacerbating the decline, as many argue to have happened in October 1987. Fortunately, Alan

Greenspan, chairman of the FED at that moment, prevented another depression in the US by not

letting commercial and investment banks to go insolvent. The market recovered and refinements

were made, such as a circuit breaker that cuts out trading programs if the market slides to a set level.

However, the nature of the global crash in 1987 leads to a more global explanation in others’

opinion31. Starting March 1987, a trade sanction policy against Japan, who was selling

semiconductors to American computer manufacturers at bargain prices, generated a violent reaction

in stock and bond markets. By August, US trade deficit at that time hit $15.8 billion, much bigger than

expected, bringing related risks of exchange-rate losses on dollar-denominated assets. At the same

time, Germany announced increases in the interest rates, regardless of the effects on the US dollar.

As US specifically noticed that no measures against dollar decline would be taken if its international

partners refuse to “stimulate demand”, the fall was inevitable: any investor holding stocks in a

currency on the way to be sharply devalued, would make all the possible to dump stocks and bonds

denominated in that currency, causing worldwide crashes.

The star of the 1990s was the collapse of the Japanese asset price bubble. The economic

bubble lasted from 1986 to 199032, in which real estate and stock prices significantly inflated. The

phenomenon originates in the policies adopted following WW2 to encourage people to save their

income.

30 Dow Jones Industrial Average fell that day with 22,61%. By the end of the month, all the major markets suffered significant declines (Hong Kong 45.8%, Australia 41.8%, Spain 31%, the United Kingdom 26.4%, the United States 22.68%, and Canada 22.5%).31 Reynolds, Alan (October 1997).” The Lessons of Black Monday”, The Wall Street Journal Europe. Alan Reynolds is director of economic research at the Hudson Institute.32 Okina, Shirakawa, and Shiratsuka (2000) define this period as the ‘bubble period’ given the coexistence of three factors of a bubble economy: a market increase in asset prices, an expansion of monetary aggregates and credit, and an over-heating of economic activity.

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Credits became much easier to obtain having more money available in banks and, on the

background of a national trade surplus, the yen appreciated against foreign currencies, giving the

local companies a competitive advantage to invest in capital resources. This made the Japanese

made products relatively cheaper and increased the trade surplus. The conditions were therefore

favourable for the financial assets to become very lucrative, making speculation inevitable, especially

in the Tokyo Stock Exchange and the real estate market. Given the high level of collusion between

the government and the business environment, the investors believed that the growth could be

sustained on a long term basis. The decline in assets prices was first seen just as a natural burst of the

bubble, amplified by the stage of the business cycle. The increase in the interest rates seemed a good

idea at the moment, but it didn’t have the effect that the government expected, instead it put

Japan’s economy down for over a decade. Things were going back to normal in 2007, but one year

later, the country was hit again by the world’s newest financial crisis

Moving West, “Black Wednesday” (September 16, 1992) is known as the day when the Bank

of England was forced to withdraw the sterling pound from the Exchange Rate Mechanism of the

European Monetary system. The exchange rate crisis was preceded33, as in most of the cases, by

hyperactivity and speculations in the foreign exchange markets.

Bank of England’s attempts to maintain the pound within the limits, either by sustain it in the

market or by increasing interest rates in order to re-gain credibility, had no effect, but to send UK’s

economy into recession, as the housing market went down and many businesses failed. Still, the

depreciation of the pound later brought an increase in exports, thus the economic recovery, despite

the central bank’s original fears that the inflation would go up34.

The 1994 Economic Crisis in Mexico 35 , also known as the Mexican peso crisis, is considered to

have started in December 1994, with the sudden devaluation of the Mexican peso. It also had a

significant impact on Argentina, Paraguay, Uruguay and Brazil.

33 Greenaway, David. (July 1997). „Policy Forum: UK Macroeconomic Policy After Black Wednesday”, Economic Journal; Vol. 107 Issue 443, p1126-11271, 2p.34 When an economy works at full capacity, a lower exchange rate would bring no benefits, as no additional net exports appears. Instead, wages and prices would go up. This was also Bank of England’s rationale of not getting out of the ERM and not allowing the pound to depreciate. Later, the costs of Black Wednesday were estimated at £3.4 billion. (Bottle, Roger. April 2008. “Pound fall is UK's get-out-of-jail-free card”, http://www.telegraph.co.uk/finance/comment/rogerbootle/2789036/Pound-fall-is-UKs-get-out-of-jail-free-card.html)35 Gil-Diaz, Francisco. „The Origin Of Mexico's 1994 Financial Crisis”, Cato Journal, Cato Institute - vol 17, no 3 (http://www.cato.org/pubs/journal/cj17n3-14.html). Francisco Gil-Diaz is General Director of Avantel, S.A., and former Vice Governor of the Bank of Mexico.

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Starting 1985, the Mexican government adopted a series of economic reforms by liberalizing

the trade sector, adopting a stabilization plan and gradually introducing market-oriented institutions.

Economic growth, stabilisation of price evolution and continuously development of the economic

strategy made the country extremely attractive for foreign investments, especially after the

successful renegotiation of its debt in 1990.

The financial sector was also liberalized, which led to a significant increase in the supply of

credit, given that, from 1990 to 1993, the country faced improved economic expectations, a

phenomenal international availability of securitized debt (Hale 1995), a boom in real estate and in

the stock market and a strong private-investment response.

However, poor borrower screening, credit-volume excesses and the slowdown of economic

growth in 1993 caused an increase in the non-performing loans while the financial burden on many

became harder to sustain. Weak supervision, unsustainable growth of bank-assets, insufficient

capitalization, lack of adequate human capital vis-à-vis the development of the financial sector, moral

hazard were among other causes that favoured the crash. The break out factors are considered to be

combination of the fixed exchange-rate regime with a rapid expansion of credit 36. Mexico lacked the

necessary foreign reserves to maintain the fixed-rate regime and the numerous bad quality credits

made the debt refinancing impossible at the end of 1994.

As other solutions didn’t exist, the Mexican government decided to let the rate to flow,

which led to a significant depreciation of the peso. Fortunately, US decided to help, first by buying

pesos in the open market and then granting $50 billion in loan guarantees. The peso stabilised and

the economy went back to the growth path.

Also called “the Asian Contagion”, the 1997 Asian financial Crisis had its origin in Thailand,

when the Thai government decided to let the baht flow from the previous fixed-rate regime against

the US dollar. The currency crisis spread rapidly in South Asia, causing stock market declines,

problems with private debt and halting import revenues.

The main underlying problems37 were undoubtedly connected to the foreign exchange

shortages, without which the currencies of Thailand, Indonesia, South Korea and other Asian

countries fell dramatically, and poorly developed financial sectors and mechanisms.

36 Bordo, M.D., and Schwartz, A.J. (1996) ``Why Clashes between Internal and External Stability Goals End in Currency Crises, 1797-1994.'' Cambridge, Mass.: National Bureau of Economic Research.37 Nanto, K. Dick (February 1998). „The 1997-98 Asian Financial Crisis” CRS Report for Congress, Federation of American Scientists (http://www.fas.org/man/crs/crs-asia2.htm).

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The crisis hit developed in two stages; first affected the Thai baht, the Malaysian ringgit, the

Philippine peso, and the Indonesian rupiah, while the second hit the Taiwan dollar, the South Korean

won, the Singaporean dollar, and the Hong Kong dollar. The Governments’ attempt to support their

currencies by selling foreign exchange reserves and raising interest rates, also caused downturns in

economic growth and made the interest-bearing securities more attractive than equities, shifting

investors’ interest towards the former and crashing the Asian stock markets.

Among other countries, US was severely affected by this crisis for several reasons. First of all,

financial markets around the world are interlinked, therefore anything affecting one market,

immediately transfers to the others. Second of all, almost any multinational company in the world,

especially from US, has a subsidiary in Asia and even more companies are involved in the financial

sector. Third, imports and exports between the Asian countries and the rest of the world (especially

US) move significantly, affecting the trade balance on the countries involved: weak Asian currencies

translate into less imports and more exports and vice-versa for their trading partners. Furthermore,

many Asian financial institutions went bankrupt, adversely affecting even stronger countries. Last,

but not least, US is a reliable partner for IMF and World Bank, through the Exchange Stabilization

Fund, therefore these multilateral agencies count on US’s capacity of providing the funds.

Some of the economists put the crisis on the policies that resulted in large quantities of

credit. Asset prices went up until they reached an unsustainable level and when they collapsed, many

companies defaulted on debt obligations making other investors withdraw. The market was flooded

with national currencies and when the local governments tried to intervene, they soon found out

they didn’t have the necessary financial power to do so. Foreign currency denominated debts

became even heavier with a currency depreciation on the background, further deepening the crisis.

Mishkin38 argues that asymmetric information is actually the underlying cause of the crisis.

Liberalization of the financial sector and the boom in the credit market caused an excessive risk-

taking39 with future large losses on loans. After the systemic collapse in both financial and non-

financial firm balance sheets, the financial markets weren’t able to channel any funds to the indeed

productive investments opportunities causing devastating effects on the economies of the respective

countries.

38 Mishkin, Frederic S.. Journal of International Money & Finance, Aug99, Vol. 18 Issue 4, p709, 15p;39 In case the credit volume increases too fast, banks can be put in the situation where they lack adequate human capital or risk-assessment systems. Asia, particularly, is also know for weak financial regulation, while investors had the impression of a government bailout in case of financial distress, adding moral hazard into the equation.

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In order to stabilize the Asian economy, the IMF initiated a $40bilion program, aimed

especially towards South Korea, Thailand, and Indonesia, but with little effect on the latter. Before

the crisis, Indonesia had actually one of the most performing economies, situation that made the

companies to borrow massively in US dollars, given the upward trend of the Indonesian rupiah. The

currency crisis hit from two directions, capital flight and balance sheet problems. The other Asian

countries benefited from local governmental programs and by 1999 analysts saw signs of recovery.

The IT bubble is widely accepted to have developed in 1998-2000, when the large public saw

the opportunities lying in the internet related companies. The period was characterised by the birth

of numerous internet based companies, referred to as dot-com’s. The development of the bubble

was a combination of venture capital, market confidence, stock speculation and upward trend in the

stock market, while investors made their bet on the technological advancements and big ideas,

rather than fundamentals and business plans.

Many of those companies applied a “get large or get lost”40 strategy, i.e. operating at a

sustained net loss to build market share, on the idea that brand awareness would permit them to

charge profitable rates later. Their main source of financing was the venture capital and the IPOs, and

given the difficulty to value the companies, the stock went skyrocketing. As only one winner could

exist on a certain niche, the venture capitalists preferred to finance several start-ups and let the

market or the luck decide.

The US FED’s restrictive monetary policy from late 1999/early 2000 caused a loss in speed for

the economy. The bubble burst was originally attributed to natural market corrections. The facts are

actually more simple - a big number of the dot-com’s reported huge losses or went bankrupt in only

months from their IPOs. The claims were indeed too big as they were expected to grow too much,

too fast.

The Global Financial Crisis of 2007 - 2009(+)

Since December 2007, the entire industrialized world has been undergoing a recession, that

was announced by the outbreak of the financial crisis of 2007–2009. The consequences took the

form of failure of key businesses, sharp declines in consumer wealth, substantial financial rescue

packages incurred by governments, and a significant decline in the economic activity41.

40 Graham, Paul (March 2005). “How to start a startup” (http://www.paulgraham.com/start.html)41 Martin Neil Baily, Douglas J. Elliott (June 2009) „The US Financial and Economic Crisis: Where Does It Stand and Where Do We Go From Here?”, Brookings,(http://www.brookings.edu/~/media/Files/rc/papers/2009/0615_economic_crisis_baily_elliott/0615_economic_crisis_baily_elliott.pdf retrieved on 10th Nov 2009)

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Similarities can be found between this crisis and the previous ones with respect to the

following factors: mismanagement of financial innovation, a bursting asset price bubble and, last,

deterioration of financial institution balance sheets42.

Economists argue that the underlying causes of this crisis lie “in the last quarter century of

deregulation and the globalization of financial markets, combined with the rapid pace of financial

innovation and moral hazard caused by frequent government bailouts”43, therefore, even if the

problems in the US subprime mortgage market started the current financial crisis, the institutional

flaws and practices of the current financial regime stayed at the bottom of the ”construction”.

Financial innovation was the main cause of the loss of liquidity that appeared in the global

financial system when the boom ended, given the complexity of the new financial products and the

difficulty in pricing them and assessing the risk involved.

On the other hand, Forbes(2009) argues that the housing bubble would have never grow the

size it did if the Federal Reserve not printed so much money and kept interest rates artificially low for

so long44, thus what started in August 2007 was not a failure of free markets, but the result of bad

government actions45.

According to Rogoff46 (2009), it`s unusual for a recession to last more than two years, but the

recovery will, indeed, take place at a slower pace in the developed countries, while the emergent

economies could experience a relatively higher growth. However, the US stimulus package would be

just a temporary boost in the economy without a proper fixing of the banking system. On the other

hand, Roubini47 (2009) considers that, although people are worried about the housing bust and

decrease of consumption, the biggest issue lies in the drop of capital spending of the companies,

therefore the stimulus package should primarily focus on diminishing the contraction in the

economic activity.

42 Livingston, James. (May/June 2009). „Their great depression and ours”, Challenge, vol. 52, no. 3, pp. 34–5143 Crotty, James (2009), “Structural Causes of the Global Financial Crisis: A Critical Assessment of the New Financial Architecture,” Cambridge Journal of Economics, 33(4), 563-580.44 Forbes, Steve (October 2009). „Capitalism: A True Love Story”, Forbes, 00156914, 10/19/2009, Vol. 184, Issue 745 In author’s opinion, other bad measures were the implementation of mark-to-market accounting rules (abolished in the spring of 2009), SEC's removal in 2007 of the uptick rule (which held that a stock couldn't be shorted unless it had gone up in price) and he rule against naked short-selling (an investor is supposed to borrow the shares before he shorts them). Idem 46 Full Session Video of Roubini, Rogoff and Behravesh Discussing Future of the Global Economy at CERAWeek® 2009 with CERA Chairman Daniel Yergin, cera.com, Cambridge. (http://www.reuters.com/article/idUS193520+27-Feb-2009+BW20090227) 47 idem

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http://www.reuters.com/article/idUS193520+27-Feb-2009+BW20090227


Crotty (2009) argues that coordinated aggressive financial regulation as well as

nationalization of financial institutions when appropriate will create the financial markets the

necessary conditions to play “a more limited but productive role in the economy”. Also, in

Livingstone’s view, rigorous regulation, or even “government ownership of the commanding

heights”, is perfectly consistent and compatible with the development of capitalism.

The same signal is sent by the European Commission: the economy needs a more ethical and

responsible financial sector, especially that effective regulation is in the interests of financial

institutions, as the prudent institutions won’t stay at the mercy of their competitors’ reckless

behaviour.

The crisis hit on US was hard: the GDP decreased at an annual rate of 6%48 in two consecutive

quarters (Q42008 and Q12009), while the unemployment rate reached 10,2%49 by October 2009, the

highest level since 1983. Still, the third semester of 2009 is widely considered to have brought a

“technical end” to the recession, with a GDP growth of 3,5%50. However, most of the growth is due to

the extensive government stimulus programs, and economists wonder what will happen after these

come to an end, as the unemployment figures still raise concern.

The Euro Area has also been severely affected51. Significant declines in the domestic demand

and external demand drive the further deterioration of the output. The economy is expected to

recover modestly, sustained by the effects of declines in risk premia, easing credit conditions and

governmental stimulus. Countries with large trade surpluses, like Germany is, have been affected by

the collapse in the world trade. The 12% fall in Italy’s exports (the worst in the region) adds on the

weak competitiveness of its exports, while Spain has better historical performance in terms of

inflation, even if the national output is lower than in previous recessions.

All the central banks took the necessary measures to prevent a total froze out of the

economy: deposit guarantees, systemic injections, lowering the key interest rates, while

48 Bureau of Economic Analysis Press Release http://www.bea.gov/newsreleases/national/gdp/gdpnewsrelease.htm) 49 Bureau of Labor Statistics, United States Department of Labor, Labor Force Statistics from the Current Population Survey, extracted on 14th December 2009.(http://data.bls.gov/PDQ/servlet/SurveyOutputServlet?data_tool=latest_numbers&series_id=LNU04000000&years_option=all_years&periods_option=specific_periods&periods=Annual+Data)50 Irvin, Neil (October 2009). „With big government boost, U.S. economy grew in 3rd quarter”, washingtonpost.com - Business, (http://www.washingtonpost.com/wp-dyn/content/article/2009/10/29/AR2009102900196.html retrieved on 20th November 2009)51 Holland, Dawn; Barrell, Ray; Fic, Tatiana; Hurst, Ian; Liadze, Iana; Orazgani, Ali and Pillonca, Vladimir (2009), “The World Economy: Recession in the Euro Area,” National Institute of Economic Review, 209, July,

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http://www.washingtonpost.com/wp-dyn/content/article/2009/10/29/AR2009102900196.html

http://www.bea.gov/newsreleases/national/gdp/gdpnewsrelease.htm


governments started corresponding fiscal policies and public spending52. The state is now forced to

act not only as lender of last resort, but as an agent of recapitalisation as well. The first country that

announced a bank recapitalisation plan was the UK and was soon followed by similar plans in other

European countries. Governments of Switzerland and Spain proposed even the buying of the bad

assets.

Moreover, the European Commission rescheduled some of the investments previously

allocated over several years; for example, the Trans-European Transport Network (TEN-T) project in

which a total of 18 projects in 11 EU member countries53 will benefit from the first round of financing

of €260 mil out of €500 mil54.

The European Union’s economy is, at least according to official data, on the edge, as small

signs of recovery can be drawn from available survey data, suggesting the recovery is continuing

during the fourth quarter of 200955. The euro area is now benefiting from the inventory cycle and a

recovery in exports, but also, from the economic stimulus and the measures aimed towards the

financial system.

José Manuel Barroso, president of the European Commission, highlights56 that there is a need

of developing new sources of growth which can take over when the stimulus is eventually

withdrawn57, as a perpetual fiscal stimulus would put unsustainable debt on the shoulders of future

generations and create a risk of an inflationary bubble that could lead to a new crisis.

However, are all these measures efficient?

The worldwide economic evolution aims towards saying yes. The econometric model

developed in the forth chapter of this paper with European data also confirms the efficiency of the

monetary policy driven by the world’s central banks. However, the observation period is rather small

and one quarter of economic growth can still be misleading.

52 A Gallup survey made between December 2008 and May 2009 shows that the EU citizens are in favor of government intervention in the economy, while Americans are found to be less favorable toward government intervention in business. (http://www.gallup.com/poll/123824/Europe-Gov-Intervention-Financial-Crises.aspx) 53 Austria, Belgium, France, Germany, Hungary, Italy, The Netherlands, Portugal, Spain, Sweden and the UK. Construction Europe, November 2009. www.khl.com/news 54 “Unlocking this funding shows the Commission is serious about tackling the economic crisis as it is targeted to encourage further economic growth. This funding released under the TEN-T programme plays a crucial role in keeping Europe moving forward.” Antonio Tajani, European Commission vice president for transport, idem55 Signs of economic growth appeared starting the third quarter when euro area quarterly growth rate of gdp was 0.42%.56 José Manuel Barroso, „Beyond the crisis”. Economist, 00130613, 11/21/2009 World in 2010 Supplement57 Moreover, the gradual phasing-out program for the financial sector implies a reduction in the number of longer term refinancing operations in the first quarter of 2010. (ECB, Monthly Bulletin, December 2009 http://www.ecb.int/pub/pdf/mobu/mb200912en.pdf)

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http://www.khl.com/news

http://www.gallup.com/poll/123824/Europe-Gov-Intervention-Financial-Crises.aspx


In Forbes’ opinion58, too many think that the government itself can create wealth, when it

only redistributes wealth, by taking resources from others: stimulus programs are only an exercise

that “use a pail to move water from one end of a pool to another, but this doesn't increase the

amount of water in the pool”. The true creator of wealth is only the private sector.

There is a broad view among the economists that the current monetary policy measures are

not effective. First of all because, given the shocks to credit markets from the financial crisis, it is

argued that the monetary policy cannot lower the cost of credit. Secondly, easing the monetary

policy during a crisis might create inflationary pressures that monetary authorities wouldn’t be able

to control59.

Mishkin (2009) disproves60 the above statements arguing that if the Federal Reserve had not

aggressively cut rates, the effect of restraining consumer spending and business investment, would

have made the economic downturn more severe, resulting in result in even greater uncertainty

about asset values with a final effect on credit spreads, causing economic activity to contract further.

However, monetary policy is not able to offset the effect of massive financial disruption in the credit

markets, this is why central banks have provided additional liquidity support to particular sectors of

the financial sectors. Like many others, Mishkin also considers that monetary policy measures are not

enough to get the financial system working again, even if they have been useful in limiting the impact

of the financial crisis. Financial institutions will have to be recapitalized sufficiently in order to have

the proper incentives to make loans to households and businesses with productive investment

opportunities. As for the stimulus packages, they must have maximum impact on the short run and

not to lead to unsustainable future tax burdens.

Fiorella De Fiore and Oreste Tristani (2009)61 also argue that an aggressive easing of policy is

optimal in response to adverse financial market shocks.

Regarding the inflation problems that might occur, it must be mentioned that the price

movements are also influenced by the public’s expectations (somehow as a self fulfilling prophecy).

The central banks must, therefore, maintain well anchored the inflation expectations by earning

58 Forbes, Steve (October 2009). „Capitalism: A True Love Story”, Forbes, 00156914, 10/19/2009, Vol. 184, Issue 759 Mishkin, Frederic S. (2009). „Is Monetary Policy Effective during Financial Crises?”, American Economic Review: Papers & Proceedings 2009, 99:2, 573–57760 Mishkin, Frederic S. (2009). „Is Monetary Policy Effective during Financial Crises?”, American Economic Review: Papers & Proceedings 2009, 99:2, 573–57761 De Fiore, Tristani (April 2009) „Optimal Monetary Policy In A Model Of The Credit Channel”, European Central Bank Working Paper Series No 1043 / April 2009

25


credibility with the financial markets and the public and maintain a certain flexibility in raising

interest rates quickly if there is an upward shift in projections for future inflation62.

Livingstone stresses out63 that the massive deflation following the 1929 crash registered the

liquidation of the “distressed assets”, but, at the same time, it halved wholesale and retail prices by

1932, and this is what the FED is trying to avoid since August 2007. Before that, Alan Greenspan

thought that the new credit instruments based on securitized assets derived from home mortgages

would eschew the issue of the “housing bubble”.

The worst outcome of this crisis is exemplified by the deflationary spiral of Japan in the

1990s, and such an outcome would cramp the equity loan market, drive down housing prices, slow

residential construction, erode consumer confidence, disrupt consumer borrowing, and reduce

consumer demand across the board. Although a weak dollar would mean lower trade and current

account deficits, even a more manageable national debt for US, it would also lower the American

demand for commodities, capital, and credit, thus the American economic leverage against the rising

powers of the East would be accordingly diminished64.

Conclusions

Looking over the last century, it comes into attention that almost all the financial crises

originated as credit driven asset bubbles. Moral hazard, weak banking regulation, herd mentality,

unsustainable growth and government spending were the ingredients that produced the collapse. I

put a special emphasize on the weak financial regulation.

We are currently experiencing a severe global financial crisis that many economists compare

to the 1929 Great Depression. The investors are now driven by one of the two predominant

sentiments on the financial markets: fear.

It is surprisingly that the world has 100 years experience in financial crises, but in reality we

didn’t learn anything. In the last 30 years, we experienced at least three crises per decade. One

would expect that, by now, the financial regulation to have achieved a high degree of reliability.

Instead we specialized in repairing, not preventing.

And the only reason that I can think of is greed.

62 idem63 Livingston, James. (May/June 2009). „Their great depression and ours”, Challenge, vol. 52, no. 3, pp. 34–5164 idem

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Chapter 3. Empirical evidence of the monetary policy effects in Europe

One of the most important issues in macroeconomics questions whether and how the

monetary policy affects the economy. Bernanke and Blinder’s model (1988) used the rather old view

of the monetary policy transmission, through which the central bank actions affect both bank

liabilities (deposits) and assets (loans). Under conditions of asymmetric information and an imperfect

substitution of loans for securities in bank portfolios, a restrictive policy leads to a decline in loans

(Blinder and Stiglitz 1983), affecting the spending of customers who dependent of bank credit and

who otherwise find it difficult to obtain financing in the open market, with a final impact on

aggregate demand.27


Still, in some previous research, the credit channel of the monetary policy transmission has

proven unsuccessful. King (1986) found that the deposits are actually better predictors of output

changes than loans. His method of research should, although, be put under question as unrestricted

vector regressions are, after all, only reduced forms, therefore the results might be biased. Bernanke

(1986) studied the relation between money, credit and income using a so called "structural vector

autoregression approach" and found a more optimistic result regarding the importance of reading,

even though, as himself acknowledges, this model is rather sensitive to the choice of specification

and to the identifying assumptions. Bernanke’s second study alternative (1992) implied the isolation

of a direct FED policy. Assuming that any change in such variables can be interpreted as a policy

shock and that, given the information lags, these shocks are independent of contemporaneous

economic disturbances, the reduced-form responses of the economy could be used to correctly

measure the structural effect of a monetary policy change.

The findings of this chapter are as follows:

(1) The GDP starts declining in the first quarter after a tightening of monetary policy,

bottoming down in the fifth quarter and going back to trend four years after the innovation. At the

same time, the prices begin to increase in the first quarter, peaking out in the fourth quarter,

decreasing rapidly the following six quarters and returning back to the trend in about four years.

After rising sharply, the ECBrate declines until the ninth quarter, bottoming out as well in about four

years (the 13th quarter more precisely).

(2) A restrictive monetary policy brings an immediate decrease of the aggregate demand,

with the maximum downturn in the fifth quarter and a return to trend in approximately four to five

years. On the other hand, the inventories build up in the first two quarters, fall in the next three

quarters and then return to trend in the following four to five years. In this case, the effect of a

tightening measure lasts more than the measure itself.

(3) A restrictive monetary policy measure has the largest effect on companies’ spending, i.e.

consumer durables (but there is also a great deal of household spending here) and investment rate .

Non-durable consumption and residential investments, in this order, are less affected in percentage

levels, given, among other factors, the nature of the goods involved and the EU economy specifics.

The European Central Bank led the most extensive empirical research in the area of

monetary policy transmission mechanism in Europe.

28


Using three different three different macroeconometric models65 of the euro area, an

empirical study of the European Central Bank66, shows that an increase in short-term interest rates

results in a temporary decrease in output, which peaks about two years after the initial monetary

policy impulse and reverts back to the baseline level thereafter, while, at the same time, prices adjust

gradually to a permanently lower level. Investments appear to be an important part of the output

changes in the situation of a monetary policy shock. The ECB’s results confirm that business

investment is sensitive both to changes in the user cost of capital and to liquidity or cash-flow effects.

Moreover, in a relatively closed economy as the euro area is, consumer and investment

expenditure, including construction and inventories are likely to be the most important. A temporary

increase in real interest rates determine the households to delay consumption and increase saving,

therefore the current domestic demand for consumer goods and services is reduced. On the other

hand, changes in interest rates also change the cost of acquiring new capital with a final effect on the

demand for fixed and inventory investment goods. Looking at a cross-sectoral level, monetary policy

effects are greater for producers of durable goods.

With regards to residential investments in the Euro Area, Alessandro Calza, Tommaso

Monacelli and Livio Stracca (2009) argue that given that the features of residential mortgage markets

differ markedly across industrialized countries, the transmission of monetary policy shocks to

residential investment and house prices is significantly stronger in those countries with larger

flexibility/development of mortgage markets; moreover, the transmission to consumption is

stronger only in those countries where mortgage equity release is common and mortgage contracts

are predominantly of the variable-rate type. A BIS67 study (1995) finds out that monetary policy has

comparatively stronger effects in Anglo-Saxon countries than in continental Europe (with the possible

exception of Italy, where variable rate mortgages predominate). According to Angeloni et al. (2003)68,

a possible explanation for the weaker response of private residential investments to monetary policy

shocks in the Euro Area comparing to US, lies in the institutional differences in housing finance.

65 The ECB’s area-wide model (AWM), the aggregations of simulations of the individual national central bank country models (NCB) and of the NiGEM multi-country model, developed by the National Institute of Economic and Social Research (NIESR)66 ECB • Monthly Bulletin • October 2002 (http://www.ecb.europa.eu/pub/pdf/other/pp43_53_mb200210en.pdf)67 Bank for International Settlements,"Financial Structure and the Monetary Policy Transmission Mechanism", C.B. 394.68 Angeloni, I., Kashyap, A. K., Mojon, B. and D. Terlizzese (2003): "The output composition puzzle: a di¤erence in the monetary transmission mechanism in the euro area and the US", Journal of Money, Credit and Banking, 35(6, Part 2), 1265-1306.

29


When discussing about investments, Benito (2004)69 argues that “inventory (dis)investment

may be an especially appealing form of adjustment, since the costs of adjusting through inventories

appear to be relatively modest compared with other mechanisms that companies may have

available”. Based on an analysis in UK (market-based financial system) and Spain (banked based

financial system), his paper concludes that movements in liquidity appear to precede those in

inventory investment by around one year in both countries. Moreover, a monetary contraction will

affect the demand for interest-sensitive sectors most, with adverse cash flow and liquidity

consequences for companies in such sectors. Their analysis shows that there is a direct influence of

the monetary policy towards investments through the level of debt servicing costs.

According to Ismail and Bahari (2007)70, increasing interest rates in the monetary tightening

periods weaken firms’ balance sheets as interest expenses also rise up, leading to an increase in the

amount of external financing that firms need, a rise in the premium on external financing that they

face, and a reduction in their accumulation of assets, their spending and their production. Based on

statistical data from Malaysian companies, they argue that, given the low adjustment cost incurred,

the firms will initially reduce inventories.

Gertler and Gilchrist (1994)71 find that small firms account for a significantly disproportionate

share of the manufacturing decline that follows a tightening of the monetary policy and play an

important role in the slowdown of the inventory demand. At the same time, large firms initially

borrow in order to accumulate inventories.

Angeloni, Kashyap, Mojon, Terlizzese (2003)72 made an extensive study about how monetary

policy affects output and prices in the U.S. and in the euro area that is extremely important and

similar with regards to the findings of this paper.

One important discovery was that although response patterns to a shift in monetary policy

are similar in most respects, they significantly differ noticeably as to the composition of output

changes. Therefore, the consumption shifts are significantly important in the US, while investment is

the predominant driver of output changes in the Euro Area. More particularly, for the Euro Area,

response of output to the monetary policy shifts is hump shaped, with the peak occurring about one

year after the shock, while whereas the price level diverges gradually but permanently from the

69 Benito, Andrew. „Financial Pressure, Monetary Policy Effects and Inventories: Firm-level Evidence from a Market-based and a Bank-based Financial System”, Economica, May2005, Vol. 72 Issue 286, p201-224,70 Ismail, Abdul Ghafar b.; Bahari, Zakaria b.. Gadjah Mada. „Monetary Policy, Debt And The Cyclical Behavior Of Inventories.” International Journal of Business, Jan-Apr2007, Vol. 9 Issue 1, p41-60, 20p71 Gertler, Mark; Gilchrist, Simon. „Monetary Policy, Business Cycles, And The Behavior Of Small Manufacturing Firms”. Quarterly Journal of Economics, May94, Vol. 109 Issue 2, p309-340.72 Angeloni, Kashyap, Mojon, Terlizzese (2003) „The Output Composition Puzzle: A Difference in the Monetary Transmission Mechanism in the Euro Area and United States”. Journal of Money, Credit, and Banking, Vol. 35, No. 6

30


initial value. The estimated investment response to monetary shocks is rather similar in both areas,

with the drop peaking about 1,5 years after the shock and a gradual return to baseline afterwards.

4.1. Economy response to a policy shock (VAR evidence)

Following this analysis, let’s consider that the economy is represented by the following

general structural model:

Yt = B0Yt + B1Yt-1 + C0Pt + C1Pt-1+ ut (1)

Pt= D0Yt + D1Yt-1 + GPt-1 + vt (2)

where,

Y= vector of non-policy variables

P=vector of policy variables

u, v= orthogonal disturbances

Furthermore, assuming there is no immediate feedback of the economy to present policy

actions, therefore excluding Yt from the second equation (D0=0) and substituting Pt in the first

equation, we obtain a standard VAR model:

Yt exclusion of (2) Pt= D1Yt-1 + GPt-1 + vt (2’)

(2) -> (1) => Yt = B0Yt + B1Yt-1 + C0(D1Yt-1 + GPt-1 + vt) + C1Pt-1 + ut (3)

Yt - B0Yt = B1Yt-1 + C0D1Yt-1 + C0GPt-1 + C0vt + C1Pt-1 + ut (3)

Yt(I - B0) = (B1+ C0D1)Yt-1 + (C0G+C1 )Pt-1 + C0vt + ut (3)

Yt= (I - B0)-1[ (B1+ C0D1)Yt-1 + (C0G+C1 )Pt-1 + C0vt + ut] (3)

Therefore, the final system (S1) is:

Yt= (I - B0)-1[ (B1+ C0D1)Yt-1 + (C0G+C1 )Pt-1 + C0vt + ut],

Pt= D1Yt-1 + GPt-1 + vt

and the effects of a policy innovation on the non-policy variables can be identified with the response

function of Y to past changes in v in the unrestricted VAR model above. Using the above model it is

31


possible to show the dynamic responses of various economic aggregates to an unanticipated

measure of the monetary policy.

In order to assess the structure of the VAR model, one must first find the nature of the

variables employed (either exogenous or endogenous).

From an econometric point of view, causality refers to the ability of one variable to predict,

and therefore cause, the other. Having two variables, yt and xt it is possible to have: (1) yt causes xt,

(2) xt causes yt, (3) there is a bi-directional causality among the variables or (4) x t and yt are

independent from each other. Two of the methods used to test and detect the cause were

developed by Granger and, respectively, Sims (1972).

The models

In order to measure the impact of a monetary policy measure, it is important to see what

happens into the economy after such measure occurs. Using a series of VAR systems on US data,

Bernanke and Gertler (1995) are able to quantify the impact of an increase in the funds rate. The

method is, basically, to identify the disturbances to the funds-rate equation in the VAR as shocks to

monetary policy and to interpret the responses of the other variables in the system as their structural

responses to an unanticipated change in the monetary policy, in this case a tightening. The results

can then be interpreted in the opposite way: the effect of a monetary easing measure on the other

variables will be inverse.

Central banks’ over-night rate73, a short term rate, is the most controlled interest rate, still

most of the investments are long-term, and therefore should depend on the long term interest rates.

Surprisingly, these investments are quite sensitive to monetary policy, mainly due to existence of

market imperfections. According to Bernanke and Gertler (1995), the credit channel is not exactly an

independent mechanism, but has more an enhancement role. As changes in the open market

interest rates tend to cause a similar effect on the external finance premium, the impact of monetary

policy upon the cost of borrowing and, in the end, on real spending, is magnified. At a broader view,

a measure of monetary tightening causes a decline in GDP and the price level at the initial impact,

followed by a decline in production, consumer goods and fixed business investment.

This paper tries to follow as closely the models applied to US in the reference paper, still, the

data available at the European Union Economic Area level is not as broad. In order to measure the

73 Blinder (1992), Christiano, Eichenbaum and Evans (1994a,b) and others employ the federal funds rate as an indicator of the stance of monetary policy.

32


effect of the European Central Bank policy in the EU EA, the following models use quarterly data from

1999:Q1 to 2009:Q2 and, at some point, proxy variables for the “original” ones.

Model 1 --- GDP, GDP deflator, Commodity index, ECB rate

The first model measures the impact of a monetary policy tightening measure on the euro

area GDP, GDP deflator and the main refinancing operation rate74 of the ECB. Therefore, the

corresponding VAR model - (S1) and equation (4) - includes the following vectors:

zt = (dlgdp dldefl dlcmdty ecbrate)-1, Γ0 = ¿)-1

Β =[ 1β12 β13β14

β21 1β23β24

β31 β32 1β34

β41β γ 42β431], Γ1 = [ γ11 γ12 γ13 γ 14

γ21 γ22 γ 23γ 24

γ31 γ32 γ33 γ 34

γ 41 γ42 γ 43 γ44] and ut = [ udlgdp t

udldfl t

udlcmdty t

uecbrate t]

Where75:

dlgdp = differential log of the volume of GDP at market price - Chain linked volumes, reference year 2000 -

ECU/euro - Seasonally and partly working day adjusted, mixed method of adjustment. Using this indicator, the

effect of inflation over the years is eliminated, so it can be considered a proxy for the real GDP.

dldefl = the GDP deflator (%YOY) SADJ. Source: Datastream, symbol EKEBGDP%E

dlcmdty = differential log of the Reuters Commodities Index. Source: Datastream, symbol RECMDTY

Real GDP and the GDP deflator are included as measures of economic activity and prices,

while the commodity price index is intended to control for oil price and other supply-side factors

influencing final output and inflation76.

The funds rate is ordered on the last position, as the model assumes that the ECB uses

contemporaneous economic information, but, at the same time, innovations in the monetary policy

do not take effect until the next period of time considered (in this case, quarter)77.

74 The European Central Bank’s interest rate on the main refinancing operations is further referred in this paper as the „ECB rate”75 See variable statistics in Appendix76 See Sims (1992) and Christiano, Sims and Zha (1993), Eichenbaum and Evans (1994b) for the rationale of including the index of commodity prices in the VAR model.77 Bernanke and Blinder (1992), Bernanke and Mihov (1995)

33


Graph 1: Moves of GDP, GDP deflator, the commodity index and ECB rate, 1999-200978

-.25

-.20

-.15

-.10

-.05

.00

.05

.10

.15

0

1

2

3

4

5

6

7

8

99 00 01 02 03 04 05 06 07 08

DLGDPDLDEFL

DLCMDTYECBRATE

In order to estimate a VAR model with these variables in the most efficient way possible, a

battery of tests must be made79 on the following test model:

dlgdp dldefl dlmdty ECBrate @ c

Lag intervals for endogenous : 1 2

Lag length selection 80 - Eviews5 calculates the model selection criteria of log-likelihood, AIC,

SIC, HQ. (function: VAR Lag Order Selection Criteria).

The most efficient lag is afterward chosen using Schwarz information criterion (SIC/SC).

In this case, the most efficient lag is 1, meaning that the endogenous variables’ lag intervals

will be 1 (i.e. Lag intervals: 1 1 tells E-views to use the first lag of all of the variables in the system as

right-hand side variables).

Also, when performing a VAR Lag exclusion Wald test81 on the above model, the use of the

second lag proves to be not significant.

Granger causality test (discussed in Appendix 7 from a theoretical point of view) can be calculated

both with the separate variables as a group (Table Granger 1 in Appendix 1 --- LS 11 dlgdp dldefl

dlmdty ECBrate @ c) and within the VAR model - lag interval 1 1 (Table Granger 2 in the same

Appendix).

Looking at Table Granger 1, the next causality relationships are valid at the 5% level:

78 Note the economic downturn in the third semester of 2008, in the worst outcome of the economic crisis worldwide.79 The entire econometric calculation of the paper was made using the Eviews5 software.80 See Appendix 1 --- LS 11 dlgdp dldefl dlmdty ECBrate @ c81 See Appendix 1 --- LS 11 dlgdp dldefl dlmdty ECBrate @ c

34

% Percentage points


GDP -> DEFL (economically relevant, the GDP deflator is by definition, the implicit price deflator

for GDP, a measure of the level of prices of all new, domestically produced, final goods and

services in an economy)

ECBrate ->GDP, GDP -> ECBrate (quite common in the VAR models and economically relevant)

ECBrate -> DEFL, DEFL -> ECB rate (again, an economically relevant, 2-ways causality relation)

ECBrate -> CMDTY, CMDTY -> ECBrate (although the causality influence is not that strong, it

exists. A commodity index is a worldwide measurement and the EU economy is one of the

largest in the world, so one can say the two variables are connected at a certain economic level)

Looking at Table Granger 2, the next causality relationships are valid at the 5% level for each

equation of the model:

GDP as endogenous/dependent variable: the significant joint probability of the other three

variables indicates a joint causality relationship.

DEFL as endogenous/dependent variable: the significant joint probability of the other three


CMDTY as endogenous/dependent variable: the joint probability is not significant at the 5%, but

we corresponding causality relationship in the case of the ECBrate (2,73%) - less likely that GDP

and inflation level in the EU area to be found to have a significant influence on a commodity

index, at least given the quarterly data considered.

ECB as endogenous/dependent variable: the significant joint probability of the other three


Conclusion: None of the variable are pure exogenous, therefore the newly equation system

looks as follows82:

LS 1 1 dlgdp dldefl dlcmdty ECBrate @ c

Estimation sample: 1999 Q1 - 2009 Q2

VAR Model - Substituted Coefficients:

===============================

DLGDP = 0.5320122469*DLGDP(-1) - 0.3164556298*DLDEFL(-1) + 0.02286728817*DLCMDTY(-1) -

0.001638743145*ECBRATE(-1) + 0.01268078931

DLDEFL = 0.1725482922*DLGDP(-1) + 0.9477142766*DLDEFL(-1) - 0.002739896388*DLCMDTY(-1) +

0.001070527115*ECBRATE(-1) - 0.002938266626

82 See Appendix 1 --- LS 11 dlgdp dldefl dlmdty ECBrate @ c for the full detailed model35


DLCMDTY = 1.506528892*DLGDP(-1) + 1.828621407*DLDEFL(-1) + 0.2645598207*DLCMDTY(-1) -

0.02445291683*ECBRATE(-1) + 0.03974734145

ECBRATE = 28.23878398*DLGDP(-1) - 20.57034771*DLDEFL(-1) + 1.275345261*DLCMDTY(-1) +

0.91181175*ECBRATE(-1) + 0.5126134291

===============================

As mentioned before, the coefficients of a VAR model are difficult to interpret, therefore, in

order to assess the impact of a monetary policy measure upon the other variables, the solution is to

estimate the impulse response function that examine the response of the endogenous variables in

the VAR model to shocks of 1 std deviation in the ECBrate. Given that GDP and prices are measured

in logs, the responses can be interpreted as proportions of the baseline levels (i.e. 0.001=0.1%).

However, as only the reduced form errors can be observed, the exact size of the shock will not be

interpreted as such, but only in terms of direction, time span and magnitude. Of course, as seen in

the graphs below, the response of the VAR variables can be also observed in a range of +/- 2 S.E.

Graph 2 - the response of the GDP and GDP deflator to a restrictive monetary policy

-.0015

-.0010

-.0005

.0000

.0005

2 4 6 8 10 12 14 16 18 20 22 24

Response of DLGDP to Nonf actorizedOne S.D. ECBRATE Innov ation

-.0004

-.0002

.0000

.0002

.0004

.0006

.0008

2 4 6 8 10 12 14 16 18 20 22 24

Response of DLDEFL to Nonf actorizedOne S.D. ECBRATE Innov ation

-.1

.0

.1

.2

.3

.4

2 4 6 8 10 12 14 16 18 20 22 24

Response of ECBRATE to Nonf actorizedOne S.D. ECBRATE Innov ation

-.003

-.002

-.001

.000

.001

.002

2 4 6 8 10 12 14 16 18 20 22 24

Response of DLGDP to ECBRATE

-.0016

-.0012

-.0008

-.0004

.0000

.0004

.0008

.0012

.0016

2 4 6 8 10 12 14 16 18 20 22 24

Response of DLDEFL to ECBRATE

-.3

-.2

-.1

.0

.1

.2

.3

.4

.5

2 4 6 8 10 12 14 16 18 20 22 24

Response of ECBRATE to ECBRATE

Response to Nonfactorized One S.D. Innovations ± 2 S.E.

The GDP starts declining in the first quarter after a tightening of monetary policy, bottoming

down in the fifth quarter. Four years after the innovation, the GDP is back to trend. Indeed, for

36


stationary VARs, the impulse responses should die out to zero and the accumulated responses should

asymptote to some non-zero constant (not shown here, but the model also verifies this condition).

The prices also start to increase in the first quarter after a monetary policy shock, peaking out

in the fourth quarter, decreasing rapidly the following six quarters and returning back to the trend in

about four years. An important note here refers to the fact that this model includes the gdp price

deflator, which, by definition has a broader range than the simple consumer price index, as it shows

the price movements for all the components of GDP and not all of them meet a decrease in

consumption after a tightening measure. As shown in one of the following models, an increase in the

ECB rate causes an increase in inventories consumption in the immediate following period that might

translate to a slight increase in prices and, in the end, of the GDP deflator.

After rising sharply, the ECBrate declines until the ninth quarter, bottoming out as well in

about four years (the 13th quarter more precisely). The results above are consisted with the pattern

observed by Bernanke and Gertler (1995) in US.

The following battery of tests confirms the validity of the above model. As a VAR model is a

combination of OLS estimations, the standard OLS assumptions of linearity, consistency and BLUness

also fit.

The VAR Residuals Heteroskedasticity tests83 suggest that there is no trace of

heteroskedasticity in the VAR model above, as none of the two White tests reject the

homoskedasticity hypothesis. The individual components of the VAR Residual Heteroskedasticity

Tests: No Cross terms (only levels and squares)84, show heteroskedasticity in the squared residual 1

(i.e. from the first equation of the VAR system). Similar to the multiple regression case, this means

that the equation is incompletely specified, i.e. another factor explaining GDP is included in the error

term. Still, including a fifth factor in the first equation means including it in the entire model, loosing

even more degrees of freedom.

Heteroskedasticity implies unequal variance of the disturbances (error terms), therefore the

β’s are still unbiased and consistent, but they are no longer efficient (BLUE), with a final impact on

the impulse response functions. The variances of the β’s are also affected, with a serious impact on

the hypothesis testing - this will lead to higher than expected t-stat and F-stat, rejecting the null

hypothesis too often.

Still if the stationarity condition is verified, the VAR model and the outcomes of the impulse

response functions should be valid. This can be check graphically (the impulse response functions

83 See Appendix 1 --- LS 11 dlgdp dldefl dlmdty ECBrate @ c84 See Appendix 1 --- LS 11 dlgdp dldefl dlmdty ECBrate @ c

37


indeed die to zero at some point in time). According to the VAR unit root test85, the model also

satisfies the stability condition.

Another issue that must be discussed is the serial independence of the residuals (i.e. cov(u t,

us)=0 for all t≠s). In case of autocorrelation, the coefficients are still unbiased and consistent, but no

longer BLUE, the estimated variances of the β’s will be biased and inconsistent and, moreover, R 2

and t-stats will be higher indicating a better fit and higher significance, respectively, than truly exists.

In this case, no evidence of serial correlation can be found neither in the residual covariation

matrix, nor in any of the twelve lags taken into consideration in the LM test 86. Note: the chosen input

of lags is a matter of professional judgement, but it must be high enough to assure a proper testing.

Having quarterly data, as in this case, requires such a number in order to check for possible yearly

correlation of the variables, for example.

At the same time, no other evidence of significant correlation can be seen in the separate

correlograms87.

In order to further check the validity of the above VAR system, it is necessary to discuss the

normality of the residuals, as not complying with this characteristic would cause serious

misspecification problems and invalidity of the tests statistics. According to the VAR residual Jarque

Bera normality test88 (skewness, kurtosis and overall JB), the model has no such problem, meaning

that the interpretation of the impulse response function is correct.

Model 2 --- Demand growth, inventories growth, GDP deflator, Commodity index, ECB rate

However, the response of the economy can be also studied in closer detail by substituting the

gdp in the original model with two of its component variables, final demand and inventories (in

growth form):

zt = (demgr invgr dldefl dlcmdty ecbrate)-1, Γ0 = ¿)-1

Β =[1 β12 β13 β14 β15

β211 β23 β24 β25

β31 β321 β34 β35

β41 β42 β431 β45

β51 β52 β53β541], Γ1 = [

γ11 γ12 γ13 γ 14 γ15

γ21 γ22 γ 23γ 24 γ 25

γ31 γ32 γ 33γ 34 γ35

γ 41 γ42 γ 43 γ44 γ 45

γ51 γ52 γ 53γ 54 γ55] and ut = [

udemgr t

uinvgr t

udldfl t

udlcmdty t

uecbrate t

]85 See Appendix 1 --- LS 11 dlgdp dldefl dlmdty ECBrate @ c86 See Appendix 1 --- LS 11 dlgdp dldefl dlmdty ECBrate @ c87 See the Appendix 6 for the correlogram graph of the paired residuals88 See Appendix 1 --- LS 11 dlgdp dldefl dlmdty ECBrate @ c

38


Where89:

demgr = Domestic demand, including stocks, contribution to quarterly GDP growth, ECB compilation based on

Eurostat’s data, seasonally and partially working day adjusted.

invgr = Changes in inventories and acquisitions less disposals of valuables, contribution to quarterly GDP

growth, ECB compilation based on Eurostat's data, seasonally and partially working day adjusted.

Looking at the structure of this “new” VAR system, the most efficient lag, according to the

Lag length selection criteria90 is 1. However, the VAR lag exclusion Wald test also validates the 1 2 lag

interval. In this case is a matter of professional judgement, but, it is preferable to take into

consideration the SIC, given the small number of observations and to keep the lag set at 1 1.

Similar, using the Granger causality tests (pairwise and withing the VAR model), we can

clarify the structure of the endogenous and exogenous variables91.

Looking at the paiwise Granger tests, the new causality relationships can be observed (the

previous cause-effect pairs are not re-taken into discussion) at a significance level of 5%:

INVGR -> DEMGR, DEMGR -> INVGR: the two variables are strongly correlated

DEMGR -> DLDEFL: as a component of GDP, the demand also influences the GDP deflator

DEMGR -> ECBrate: the demand level shows the state of the economy so the ECBrate to be

taken accordingly

DLCMDTY -> DEMGR: the commodity prices strongly influence the demand growth

DLCMDTY -> INVGR: the commodity prices strongly influence the inventories growth

ECBrate -> DLDEFL, DLDEFL -> ECBrate

ECBrate -> DLCMDTY, DLCMDTY -> ECBrate

When looking at the overall VAR Granger causality Tests, the results are quite similar,

excepting the case of the equation having INVGR as a dependent variable, whose “irrelevance” is

accepted at the level of 8,24%. Also, in the VAR system, the commodity index doesn’t appear to be

significant as a dependent variable, but, as discussed before, it will be treated as such given the

dependency relationship between the ECB rate and the index, already discussed above in the first

model.

89 See variable statistics in Appendix90 See Appendix 2 --- LS 11 demngr, invgr, dldefl, dlmdty ECBrate @ c91 See Appendix 2 --- LS 11 demngr, invgr, dldefl, dlmdty ECBrate @ c for the detailed information on the two tests

39


Given the small p-value92 of the INVGR equation that is, however, under 10%, and the results

of the previous pairwise Granger tests, the following VAR system remains valid93:

LS 1 1 demgr invgr dldefl dlcmdty ECBrate @ c



===============================

DEMGR = 0.4136874647*DEMGR(-1) - 0.7370554199*INVGR(-1) - 0.287298377*DLDEFL(-1) +

0.03530726785*DLCMDTY(-1) - 0.0007228544836*ECBRATE(-1) + 0.009109795903

INVGR = 0.1382121561*DEMGR(-1) - 0.5254195152*INVGR(-1) - 0.001511475928*DLDEFL(-1) +

0.01420211545*DLCMDTY(-1) + 8.032360384e-005*ECBRATE(-1) - 0.001333241208

DLDEFL = 0.1073314139*DEMGR(-1) + 0.02738353354*INVGR(-1) + 0.8852825632*DLDEFL(-1) +

0.002768528869*DLCMDTY(-1) + 0.001263272102*ECBRATE(-1) - 0.002031060547

DLCMDTY = 0.2091118985*DEMGR(-1) - 3.949805003*INVGR(-1) + 0.738973445*DLDEFL(-1) +

0.3647187123*DLCMDTY(-1) - 0.0213953812*ECBRATE(-1) + 0.05565619889

ECBRATE = 18.04248518*DEMGR(-1) - 34.59068916*INVGR(-1) - 32.80947516*DLDEFL(-1) +

2.38264877*DLCMDTY(-1) + 0.9504695773*ECBRATE(-1) + 0.6684753912

===============================

-.0012

-.0010

-.0008

-.0006

-.0004

-.0002

.0000

.0002

2 4 6 8 10 12 14 16 18 20 22 24

Response of DEMGR to Nonf actorizedOne S.D. ECBRATE Innovation

-.00020

-.00016

-.00012

-.00008

-.00004

.00000

.00004

2 4 6 8 10 12 14 16 18 20 22 24

Response of INVGR to Nonf actorizedOne S.D. ECBRATE Innovation

-.1

.0

.1

.2

.3

.4

2 4 6 8 10 12 14 16 18 20 22 24


92 A possible explanation for these test statistics could be the small number of observations, given that in case of the pairwise tests, the causality relationship exists.93 See Appendix 2 --- LS 11 demngr, invgr, dldefl, dlmdty ECBrate @ c for the full detailed model

40


-.0020

-.0015

-.0010

-.0005

.0000

.0005

.0010

2 4 6 8 10 12 14 16 18 20 22 24

Response of DEMGR to ECBRATE

-.0006

-.0004

-.0002

.0000

.0002

.0004

.0006

2 4 6 8 10 12 14 16 18 20 22 24

Response of INVGR to ECBRATE

-.3

-.2

-.1

.0

.1

.2

.3

.4

.5

2 4 6 8 10 12 14 16 18 20 22 24



According to the impulse response functions above, the demand decreases rapidly after a

monetary policy tightening measure, with the maximum downturn in the fifth quarter and going back

to trend in approximately four to five years. The inventories build up in the first two quarters after an

increase in the ECBrate, fall in the next three quarters and then return to trend in the following four

to five years94. Following a sharp rise, the ECBrate declines until the eleventh quarter, bottoming out

relatively sooner, in just four years. These results are again, quite similar to the ones obtained by

Bernanke and Gertler (1995) for US and the ones obtained in the ECB working papers

In this case as well, the validity of the results is confirmed by the test statistics, the VAR

model being stable, with no heteroskedasticity , serial correlation or normality issues95:

Analyzing the serial correlation of the residuals, the only problem that appears is a

presumptively serial correlation issue at lag 6. Still, as the covariance matrix of the residuals shows no

significant relationship among them, this correlation issue could be caused by the nature of the data.

However, the presence of autocorrelation reduces the efficiency of both the estimations and the

impulse response function, even though, in this case, the effect is rather small as the test statistics

are still significant.

Again, the skewness, kurtosis and the overall Jarque Bera test confirm the hypothesis of

normal residuals which validates the movement showed by the impulse response function96.

Model 3 --- durables consumption, nondurable consumption, residential investment, GDP deflator,

Commodity index, ECB rate

To see what happens even in more detail, the gdp in the original model was replaced by

some important components of the private domestic spending: consumer durables (proxied by an

94 Consistent with Blinder and Maccini (1991) view of the importance of inventory disinvestments in the recession periods 95 See Appendix 2 --- LS 11 demngr, invgr, dldefl, dlmdty ECBrate @ c for full detailed statistics96 See Appendix 2 --- LS 11 demngr, invgr, dldefl, dlmdty ECBrate @ c for the full normality statistics

41


index of durables consumption), non-durable consumption (proxied by an index of non-durables

consumption), residential investment (proxied by an index of residential construction cost97) and

business fixed investment (proxied by the investment rate), as follows.

zt = (inddur indnondur rescostindex invrate dldefl dlcmdty ecbrate) -1,

Γ0 = ¿)-1

LS 1 1 inddur indnondur rescostindex invrate dldefl dlcmdty ecbrate @ c

Β =[1 β12 β13β14β15 β14 β15

β211 β23β24β25 β14 β15

β31β321 β34β35 β14 β15

β41β42 β431 β45 β46 β47

β51β52 β53 β54 1β56β57

β61β62 β63 β64 β651β67

β71β72 β73 β74 β75 β761], Γ1 = [

γ 11γ12 γ 13γ 14 γ15 γ 16 γ17

γ21 γ 22γ 23 γ24 γ 25γ 26 γ27

γ31 γ 32γ 33 γ34 γ 35γ 36 γ37

γ 41 γ42 γ 43γ 44 γ 45 γ 46γ 47

γ51 γ 52γ 53 γ54 γ 55γ 56 γ57

γ61 γ 62γ 63 γ64 γ 65γ 66 γ67

γ71 γ 72γ 73 γ74 γ 75γ 76 γ77

] and ut = [uinddur t

u indnondur t

urescostindex t

uinvratet

udldfl t

udlcmdty t

uecbrate t

]Where98:

inddur = EK INDUSTRIAL PRODUCTION - CONSUMER DURABLES (EA16) VOLA INDEX (2000=100) Source:

Datastream (symbol EKESICODG), quarterly data. Durable Consumer Goods: Goods which have long life span

and usage period. Examples: furniture, kitchenware, consumer electronics. (first lag values QtQ)

indnondur = EK INDUSTRIAL PRODUCTION - CONSUMER NON-DURABLES (EA16) VOLA INDEX (2000=100).

Source: Datastream (symbol EKESICNDG), quarterly data. Non Durable Consumer Goods: Goods have a very

short life span and are perishable in nature. Example: food, consumables. (first lag values QtQ)

rescostindex = EK NEW RESIDENTIAL BUILDINGS - COST INDEX (EA16) NADJ INDEX (2000=100). Source:

Datastream (symbol EKEQEIBCF), quarterly data. (first lag values QtQ)

invrate = The investment rate shows investments in fixed assets (mainly machinery and buildings) as a

percentage of value added created in the production process. EK INVESTMENT RATE - NONFINANCIAL

CORPORATIONS SADJ, source Datastream (symbol EKESCINVQ), quarterly data. (first lag values QtQ)

resper = EK NEW RESIDENTIAL BUILDINGS - PERMITS INDEX (EA16) NADJ (2000=100). Source: Datastream

(symbol EKEQEIBUF), quarterly data. Not discussed in the paper. (first lag values QtQ)

To compute the new VAR system, the first step is to select the appropriate lag. The test

below confirms as valid the first lag (SIC criterion)99.

97 Using an index of residential building permits would give similar results.98 See variable statistics in Appendix. The model computes the dl changes in the variables.99 In the base paper, Ben Bernanke uses 12 lags for each variable i.e. 12 months back. This model has significantly less data, therefore the tests suggest as appropriate the use of 1 lag, corresponding to 4 months back. See the Appendix 3 --- LS 11 inddur indnondur rescostindex invrate dldefl dlmdty ECBrate @ c for full

42


Again, the VAR lag exclusion Wald test also validates the 1 2 lag interval. However, it is

preferable to take into consideration the SIC, given the small number of observations and the large

number of variables and to keep the lag set at 1 1, in order not to lose too many degrees of freedom.

The next step of the modelling is to establish which of the variables are endogenous and

which are exogenous, according to Granger causality test, computed both pairwise and overall100.

INDDUR -> INDNONDUR: economically viable, the reverse is of course not valid, usually,

nondurable goods have a rather inelastic demand as they cover daily (or other short term)

necessities.

INDDUR -> RESCOSTINDEX: there is a close relationship between the index of durables

consumption and the residential cost index, as any modification of the first has a significant

impact on the second.

INDDUR -> INVRATE: similar to the previous relationship, in this case, the behaviour of the

companies towards the investments made is strongly influenced by consumption (therefore

the cost) of the durable goods. The reverse relationship is validated at a 10% level, meaning,

if accepted, that the investment rate influences the index of durable consumption.

INDDUR -> ECBrate: economically viable, the investments in durable goods show the health

of an economy, therefore, the decision regarding the key rate takes into consideration this

aspect.

INDDUR ->DEFL: economically viable as the GDP deflator also contains the durable goods.

INVRATE -> INDNONDUR: viable as these two categories do include common goods

DLCMDTY -> RESCOSTINDEX, RESCOSTINDEX -> DLCMDTY: the cost index of the residential

constructions is indeed influenced by the related commodity prices, hence the commodity

index and vice-versa.

ECBrate -> INVRATE, INVRATE -> ECB: economically viable, the two variables influence each

other.

INVRATE -> INDDUR(significant at 10%), INDDUR ->INVRATE: the relationship is obvious as

the higher the companies choose to invest in fixed assets, the higher the index of durable

goods consumption is, due to the common goods.

In the VAR Granger causality tests, the ECBrate, DLCMDTY and INVRATE equations are

validated at a high significance level (below 1%), while INDDUR, INDNONDUR are accepted at a 10%

lag selection statistics.100 See the Appendix 3 --- LS 11 inddur indnondur rescostindex invrate dldefl dlmdty ECBrate @ c for full Granger tests

43


significance level. RESCOSTINDEX and DLDEFL are overall not significant as endogenous variables in

this structure, but they are still influenced by some of the variables at a pairwise level.

Given the above, the initial structure of the VAR system is kept, as follows101:

LS 1 1 inddur indnondur rescostindex invrate dldefl dlcmdty ecbrate @ c



===============================

INDDUR = 0.3601670411*INDDUR(-1) - 0.4516392395*INDNONDUR(-1) - 1.103059301*RESCOSTINDEX(-1) +

0.2895670844*INVRATE(-1) - 0.8011743508*DLDEFL(-1) + 0.004822095412*DLCMDTY(-1) -

0.005744931798*ECBRATE(-1) + 0.04041829296

INDNONDUR = 0.2966742403*INDDUR(-1) - 0.4631367857*INDNONDUR(-1) - 0.2507409883*RESCOSTINDEX(-

1) + 0.3751421979*INVRATE(-1) + 0.4747997036*DLDEFL(-1) - 0.014338536*DLCMDTY(-1) -

0.0006853159282*ECBRATE(-1) - 0.00276491774

RESCOSTINDEX = 0.1447823387*INDDUR(-1) - 0.021267445*INDNONDUR(-1) +

0.08544901899*RESCOSTINDEX(-1) + 0.02937365891*INVRATE(-1) + 0.0614231726*DLDEFL(-1) +

0.02437110439*DLCMDTY(-1) - 0.000331019967*ECBRATE(-1) + 0.006831636174

INVRATE = 0.5062999382*INDDUR(-1) - 0.2589953681*INDNONDUR(-1) - 0.1869570896*RESCOSTINDEX(-1) -

0.037506665*INVRATE(-1) - 0.121869344*DLDEFL(-1) + 0.01229902241*DLCMDTY(-1) -

0.004334375012*ECBRATE(-1) + 0.01873220733

DLDEFL = - 0.02080440561*INDDUR(-1) + 0.01414341193*INDNONDUR(-1) - 0.05879900054*RESCOSTINDEX(-

1) - 0.03090116161*INVRATE(-1) + 0.7599188424*DLDEFL(-1) + 0.001334925*DLCMDTY(-1) +

0.0009465615114*ECBRATE(-1) + 0.002740943047

DLCMDTY = 1.846331892*INDDUR(-1) - 1.131532475*INDNONDUR(-1) - 5.124500821*RESCOSTINDEX(-1) +

1.177877934*INVRATE(-1) + 4.039900105*DLDEFL(-1) + 0.2716752781*DLCMDTY(-1) -

0.01277488382*ECBRATE(-1) + 0.01382508649

101 See the Appendix 3 --- LS 11 inddur indnondur rescostindex invrate dldefl dlmdty ECBrate @ c for the full detailed model

44


ECBRATE = 11.98971305*INDDUR(-1) - 5.852375839*INDNONDUR(-1) - 2.360844134*RESCOSTINDEX(-1) +

5.54869593*INVRATE(-1) - 15.86115023*DLDEFL(-1) + 0.8844088544*DLCMDTY(-1) +

0.9626978314*ECBRATE(-1) + 0.4593735397

===============================

The impulse response functions below show the responses of these spending components to

a monetary policy shock through the impulse response functions:

-.004

-.003

-.002

-.001

.000

.001

5 10 15 20 25 30 35 40 45

Response of INDDUR to Nonf actorizedOne S.D. ECBRATE Innov ation

-.0012

-.0010

-.0008

-.0006

-.0004

-.0002

.0000

.0002

5 10 15 20 25 30 35 40 45

Response of INDNONDUR to Nonf actorizedOne S.D. ECBRATE Innov ation

-.0010

-.0008

-.0006

-.0004

-.0002

.0000

.0002

5 10 15 20 25 30 35 40 45

Response of RESCOSTINDEX to Nonf actorizedOne S.D. ECBRATE Innov ation

-.0030

-.0025

-.0020

-.0015

-.0010

-.0005

.0000

.0005

5 10 15 20 25 30 35 40 45

Response of INVRATE to Nonf actorizedOne S.D. ECBRATE Innov ation

-.1

.0

.1

.2

.3

.4

5 10 15 20 25 30 35 40 45


-.008

-.006

-.004

-.002

.000

.002

.004

5 10 15 20 25 30 35 40 45

Response of INDDUR to ECBRATE

-.0025

-.0020

-.0015

-.0010

-.0005

.0000

.0005

.0010

5 10 15 20 25 30 35 40 45

Response of INDNONDUR to ECBRATE

-.0020

-.0016

-.0012

-.0008

-.0004

.0000

.0004

.0008

5 10 15 20 25 30 35 40 45

Response of RESCOSTINDEX to ECBRATE

-.005

-.004

-.003

-.002

-.001

.000

.001

.002

5 10 15 20 25 30 35 40 45

Response of INVRATE to ECBRATE

-.3

-.2

-.1

.0

.1

.2

.3

.4

.5

5 10 15 20 25 30 35 40 45



This result is similar to the one obtained by Bernanke and Blinder (1995) for US and ECB

studies in Europe.

The nondurable consumption has a moderate decrease, with the worst outcome in the third

quarter after the shock. After a recovery period of about 15 quarters, the indicator is virtually back to

trend. The residential construction reach and remain at the bottom in the fourth and fifth quarter,

recovering afterwards. At a percentage level, is the least affected component. The investment rate, 45


on the other hand, reaches and stays at the bottom in the second semester after a monetary

tightening measure, being the second most affected after the durable consumption, the effect is

afterwards removed after aprox. 18 quarters. An important note here is that the movement of

components above is levelled as well by their proportions in the overall spending.

The conclusion drawn from the facts above is that a monetary policy measure has the largest

effect on companies’ spending, i.e. consumer durables (but here there is also a great deal of

household spending) and investment rate . Non-durable consumption and residential investments

are less affected (in percentage levels) given, among other factors, the nature of the goods involved.

Bernanke and Blinder obtained a rather different result in this analysis: residential

investment drops sharply following a monetary tightening, next in importance are consumer

durables and nondurables, while business fixed investment also declines following a monetary

tightening, but with a greater lag than other types of spending. Still, the US economy is rather

different than what we have in the European Union, especially in the case of residential investments

(i.e. the housing market), therefore the non-correspondence is viable.

Using US data, Oliner and Rudebusch (1996)102 showed that tight money increases the

sensitivity of investment with respect to internal liquidity for small firms; whereas for large firms the

influence is not statistically different from zero.

Guariglia (1999)103 studied inventory investment (1968–91) in United Kingdom, and found

that firms with low coverage ratios or high debt ratios, experience greater sensitivity of inventory

investment to the coverage ratio in recessions and periods of tight monetary policy. Angelopoulou

and Gibson’s results104 indicate that cash flow sensitivity in financially constrained firms is higher

during periods of tight monetary policy and that financial constraints weaken with financial market

sophistication.

Discussing the validity of the model, again, the test statistics show that the VAR model is

stable, with no heteroskedasticity , serial correlation or normality issues.

The VAR residual Serial Correlation Test confirms the absence of serial correlation among the

residuals, with the exception of lag 8; however, as all the other test confirm the model as valid, the

102 Oliner, S. D. and Rudebusch, G. D. (1996). Is there a broad credit channel for monetary policy? Economic Review (Federal Reserve Bank of San Francisco), no. 1, 3–13.103 Guariglia, A. (1999). The effects on financial constraints on inventory investment: evidence from a panel of UK firms. Economica, 66, 43–62.104 Angelopoulou, Eleni; Gibson, Heather D (Oct2009), Economica, Vol. 76 Issue 304, p675-703, 29p, 12 charts, 1 graph;

46


result can be put on the nature of the data employed. Covariance matrix of the residuals shows

almost null values, making valid the interpretation of the impulse response functions above105.

As seen in the Appendix 3 --- LS 11 inddur indnondur rescostindex invrate dldefl dlmdty

ECBrate @ c, the overall Jarque Bera tests confirm the hypothesis of normal residuals which validates

the movement showed by the impulse response function, even though there seems to be a small

variance problem pointed out by the kurtosis of the residuals106.

4.2. The Credit Channel of the Monetary Policy Transmission

Introduction

The standard view of the monetary policy transmission mechanism, also known as the

„money view”, explains this mechanism through the interest rate channel, being widely accepted.

Still, this view cannot completely explain by itself the transmission mechanism (Bean, Larsen and

Nikolov, 2002), as it ignores the possibility of financial frictions. These are covered by the theory,

which emphasises the role of the credit channel. This theory stresses that the external finance

premium, defined as the cost difference between external financing (equity or debt issue) and

internal financing (retained earnings) amplifies the direct effects of monetary policy on interest rates.

Central banks’ over-night rate, a short term rate, is the most controlled interest rate, still

most of the investments are long-term, and therefore should depend on the long term interest rates.

Surprisingly, these investments are quite sensitive to monetary policy, mainly due to existence of

market imperfections. According to Bernanke and Gertler (1995), the credit channel is not exactly an

independent mechanism, but has more an enhancement role. As changes in the open market

interest rates tend to cause a similar effect on the external finance premium, the impact of monetary

policy upon the cost of borrowing and, in the end, on real spending, is magnified.

At the companies’ level, a tight monetary policy weakens borrowers' balance sheet, mainly

the cash-flow level and the asset prices.

Gertler and Gilchrist (1993, 1994) studied the differential impact of a cash squeeze on

different types of firms and discovered that the impact depends largely on firms' ability to

compensate the drop in cash flows by borrowing. Firms that have relatively poor access to credit

markets may have to respond to declining cash flows by cutting production and employment, while

105 See the Appendix 3 --- LS 11 inddur indnondur rescostindex invrate dldefl dlmdty ECBrate @ c for full statistics.106 As all the overall tests are conclusive to the original hypothesis, small, partial inadvertences can be put on the nature of the data used.

47


firms with good access to credit will face less financial pressure. Moreover, big and stable companies

may appeal to stock markets to obtain financing, while this channes is relatively closed to the small

firms.

Ehrmann and Fratzscher107 (2004) found out that firms with low cash flows, small size, poor

credit ratings, low debt to capital ratios or high price-earnings ratios are affected significantly more

by monetary policy.

Kashyap, Stein, and Wilcox (1993)108 for US and De Haan and Sterken (2006)109 for Europe,

show that a tightening of monetary policy has a particularly strong impact on firms that are highly

bank dependent borrowers as banks reduce their overall supply of credit.

Using the same VAR technique, the following model was employed for several countries in

the Euro Area, in two industries: manufacturing and wholesale & retail, using yearly data. Given the

small number of observations, the models and the response functions cannot be guaranteed to be

statistically relevant, but assuming they are, some relevant conclusions can be drawn from here.

In case of monetary policy tightening measure, the manufacturing companies from Italy and

Portugal meet similar moves with regards to profitability, interest and employee expenses (i.e.

decrease, increase and respectively, increase), while Germany, although records increases in the

interest expenses, shows increases in profitability and decreases in the employment benefits.

At the same time, the wholesale & retail trading companies show a decrease in the employee

benefits and increases in profits for all the three countries taken into consideration. In case of

interest expenses, Portuguese companies meet an increase (probably given the economy specifics

and the national financial system), while German and Italian W&R firms could be able to switch on

trade credit (these two countries also have a strong trading partnership, both with each other and

with other countries in the Euro Area like Spain and France).

The results obtain are highly consistent with the economic profile of the countries in

question. All three of them are members of the Euro Area but they have a different grade of

development. Portugal still has competitivity problems, a rather weak industry, corruption issues and

unemployment. Italy stays better regarding the industry and exports but poor R&D and corruption

can halt these advantages on the long run. Germany is a highly industrialized economy, but the

eastern part is still going through a modernisation and integration process, meant to last until 2019.

107 Ehrmann , Michael. Fratzscher, Marcel (August 2004). „Taking Stock: Monetary Policy Transmission to Equity Markets”, Journal of Money, Credit, and Banking, Vol. 36, No. 4 108 Kashyap, Anil K., Jeremy C. Stein, and David W. Wilcox (1993). "Monetary Policy and Credit Conditions: Evidence from the Composition of Extemal Finance." American Economic Review 83, 78-98.109 De Haan and Sterken (July 2006), “The Impact of Monetary Policy on the Financing Behaviour of Firms in the Euro Area and the UK”, The European Journal of Finance, Vol. 12, No. 5, 401–420

48


Empirical research

Performing the lag exclusion tests on each of the following VAR model, the most significant

lag for the analysis is found to be either x= 1 or 2 depending on country, variables or industry.

LS 1 x manempl, ecbrate @ c

LS 1 x manint, ecbrate @ c

LS 1 x manprof, ecbrate @ c

And, respectively

LS 1 x wrempl, ecbrate @ c

LS 1 x wrint, ecbrate @ c

LS 1 x wrprof, ecbrate @ c

Where:

mcov = (using Bernanke and Gertler method) the ratio of interest payments by nonfinancial corporations to the

sum of interest payments and profits, manufacturing industry, Δ yearly data, BACH database110

mempl = staff costs in % of turnover, manufacturing industry, Δ yearly data, BACH database

wrint = interest paid on financial debts in % of turnover, manufacturing industry, Δ yearly data, BACH database

mprof = profit or loss for the financial year in % of turnover, manufacturing industry, Δ yearly data, BACH

database

wrcov = (using Bernanke and Gertler method) the ratio of interest payments by nonfinancial corporations to

the sum of interest payments and profits, wholesale and retail trading, yearly data, BACH database

wrempl = staff costs in % of turnover, wholesale and retail trading, yearly data, BACH database

wrint = interest paid on financial debts in % of turnover, wholesale and retail trading, yearly data, BACH

database

wrprof = profit or loss for the financial year in % of turnover, wholesale and retail trading, yearly data, BACH

database

ecbrate @ c = the ECB main refinancing operation interest rate, yearly data, European Central Bank

Countries:

110 BACH is a database containing harmonised annual accounts statistics of non-financial enterprises. The database was set up in 1987, in co-operation with the European Committee of Central Balance Sheet Data Offices ECCB, with a view to supplement other information sources on company sector already existing at European level. The reporting countries are Belgium, Germany, Spain, France, Italy, Netherlands, Austria, Finland, Portugal, USA and Japan. Accounts are "harmonised" through a common layout for balance sheets, profit and loss accounts, statements of investments and statements of depreciation.

49


Portugal

Country economic profile according to Wikipedia: “Portugal's economic development model has

been changing from one based on public consumption and public investment to one focused on exports,

private investment, and development of the high-tech sector. Business services have overtaken more

traditional industries such as textiles, clothing, footwear, cork (of which Portugal is the world's leading

producer)111, wood products and beverages112. Portugal has a strong tradition in the fisheries sector and is one

of the countries with the highest fish consumption per capita113.” However, the industry has performance

issues, while the unemployment and corruption remain some of the most important problems.

(http://en.wikipedia.org/wiki/Portugal#Economy)

Germany

Country economic profile according to Wikipedia: “Germany is the largest national economy in

Europe, the fourth largest by nominal GDP in the world, and ranked fifth by GDP (PPP) in 2008 114. Since the age

of industrialisation, the country has been a driver, innovator, and beneficiary of an ever more globalised

economy. Most of the country's products are in engineering, especially in automobiles, machinery, metals, and

chemical goods115. Germany is the leading producer of wind turbines and solar power technology in the world.

The largest annual international trade fairs and congresses are held in several German cities such as Hanover,

Frankfurt, and Berlin.”

Italy

Country economic profile according to Wikipedia: “The country is the world's sixth highest

exporter, with US$546.9 billion exports in 2008116, and the fifth world's largest industrial goods producer with a

US$381 billion output in 2008117. Also, the country exports and produces the highest level of wine118, exporting

over 1,793 tonnes. Italy was responsible for producing approximately one-fifth of world wine production in

2005119.

Italy's major exports are precision machinery, motor vehicles (utilitaries, luxury vehicles, motorcycles,

scooters), chemicals and electric goods, but the country's more famous exports are in the fields of food and

clothing.” (www.wikipedia.com/Italy)

111 Grande Enciclopédia Universal, p. 10543, "Portugal", para. 4112 Investing in Portugal Report, Financial Times113 (Portuguese) PESSOA, M.F.; MENDES, B.; OLIVEIRA, J.S. CULTURAS MARINHAS EM PORTUGAL114 Rank Order – GDP (purchasing power parity) CIA Factbook 2005. Retrieved 31 December 2006.115 "CIA Factbook". https://www.cia.gov/library/publications/the-world-factbook/geos/gm.html. Retrieved 2009-08-02.116 CIA - The World Factbook - Country Comparison :: Exports". Cia.gov. https://www.cia.gov/library/publications/the-world-factbook/rankorder/2078rank.html. Retrieved 2009-10-27.117 GDP Sector composition: Field Listing - GDP composition by sector - United Nations118 "Faostat". Faostat.fao.org. 2009-06-23. http://faostat.fao.org/site/567/DesktopDefault.aspx?PageID=567#ancor. Retrieved 2009-10-27119 Mulligan, Mary Ewing and McCarthy, Ed. Italy: A pasion for wine. , 2006, 62(7), 21-27

50

http://en.wikipedia.org/wiki/Portugal#Economy


The method of analysis being similar to the first part of this chapter, we shall now look at the

impulse response function, as follows120:

Portugal - manufacturing industry:

.015

.020

.025

.030

.035

.040

.045

.050

.16

.20

.24

.28

.32

.36

.40

.44

1999 2000 2001 2002 2003 2004 2005 2006 2007

MANCOV ECBRATE

As it can be seen in the graph above, there is a close relationship between the ECB rate and

the coverage ratio. Note: the data corresponds the period before the financial crisis of 2007-2009

and the effects of the tightening measure on the coverage ratio are almost 1 year lagged. Note2: the

manufacturing industry has a close connection with the banking sector with regards to activity

financing.

-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20 22 24

Response of MANEMPL to NonfactorizedOne S.D. ECBRATE Innovation

-.002

.000

.002

.004

.006

.008

.010

.012

2 4 6 8 10 12 14 16 18 20 22 24

Response of ECBRATE to NonfactorizedOne S.D. ECBRATE Innovation

-.06

-.04

-.02

.00

.02

.04

.06

.08

2 4 6 8 10 12 14 16 18 20 22 24

Response of MANEMPL to ECBRATE

-.008

-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20 22 24



The employee benefits increase in the second year and returning back to trend in about 6

years following a monetary policy tightening.

.000

.001

.002

.003

.004

.005

.006

.007

2 4 6 8 10 12 14 16 18 20 22 24

Response of MANINT to NonfactorizedOne S.D. ECBRATE Innovation

.000

.002

.004

.006

.008

.010

.012

2 4 6 8 10 12 14 16 18 20 22 24


-.3

-.2

-.1

.0

.1

.2

.3

2 4 6 8 10 12 14 16 18 20 22 24

Response of MANINT to ECBRATE

-.010

-.005

.000

.005

.010

.015

.020

2 4 6 8 10 12 14 16 18 20 22 24



The interest rate expenses increase after a monetary policy tightening, reaching a maximum

in the third year and returning back to trend in about 8 years.

120 The test statistics are significant given the chosen structure of the VAR model, and can be tested within the Eviews workfiles attached. The VAR equations can be seen in the Appendix 7.

51


-.016

-.012

-.008

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20 22 24

Response of MANPROF to NonfactorizedOne S.D. ECBRATE Innovation

-.0005

.0000

.0005

.0010

.0015

2 4 6 8 10 12 14 16 18 20 22 24


-.06

-.04

-.02

.00

.02

.04

.06

2 4 6 8 10 12 14 16 18 20 22 24

Response of MANPROF to ECBRATE

-.0015

-.0010

-.0005

.0000

.0005

.0010

.0015

.0020

2 4 6 8 10 12 14 16 18 20 22 24



The profitability of the manufacturing firms decrease immediately after a monetary policy

tightening measure, with the maximum downturn in the second year, and return back to trend in

about 10 years.

Portugal - wholesale & retail trading

.015

.020

.025

.030

.035

.040

.045

.050

.24

.26

.28

.30

.32

.34

.36

.38

1999 2000 2001 2002 2003 2004 2005 2006 2007

WRCOV ECBRATE

As can be seen in the graph above, the coverage ratio has a tight connection with the ECB

rate. In the wholesale & retail trading, cash is almost immediate. If a company needs financing, it will

appeal to a short term loan, so the coverage ratio depends more on the variable interest rate on the

market, fact shown by the non-existence of a lag in the relationship between the two variables. The

second option of such a company is to appeal to trade credit, case in which the indicator should

actually decrease.

52


-.08

-.04

.00

.04

.08

.12

2 4 6 8 10 12 14 16 18 20 22 24

Response of WRINT to NonfactorizedOne S.D. ECBRATE Innovation

-.004

-.002

.000

.002

.004

.006

.008

2 4 6 8 10 12 14 16 18 20 22 24


-.3

-.2

-.1

.0

.1

.2

.3

.4

2 4 6 8 10 12 14 16 18 20 22 24

Response of WRINT to ECBRATE

-.016

-.012

-.008

-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20 22 24



The interest rate expenses of the W&R trading companies reach a maximum in increase in

the 2nd year after a monetary policy tightening measure, returning back to trend in about 4 years.

-.030

-.025

-.020

-.015

-.010

-.005

.000

.005

2 4 6 8 10 12 14 16 18 20 22 24

Response of WREMPL to NonfactorizedOne S.D. ECBRATE Innovation

-.002

.000

.002

.004

.006

.008

.010

.012

2 4 6 8 10 12 14 16 18 20 22 24


-.10

-.08

-.06

-.04

-.02

.00

.02

.04

2 4 6 8 10 12 14 16 18 20 22 24

Response of WREMPL to ECBRATE

-.010

-.005

.000

.005

.010

.015

.020

2 4 6 8 10 12 14 16 18 20 22 24



The employee expenses of the W&R trading companies decrease after a monetary policy

tightening measure, with the maximum downturn in the 2nd semester, returning back to trend in

about 1,5 years. Note the difference in direction between this industry and the manufacturing

industry. A possible reason is that the W&R trading companies need immediate cash in order to

resupply and might resort to expenses cuts, while the manufacturing companies (having mostly

skilled workers) cannot resort to this solution as they might risk problems in activity.

-.12

-.08

-.04

.00

.04

.08

2 4 6 8 10 12 14 16 18 20 22 24

Response of WRPROF to NonfactorizedOne S.D. ECBRATE Innovation

-.004

-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20 22 24


-.3

-.2

-.1

.0

.1

.2

.3

2 4 6 8 10 12 14 16 18 20 22 24

Response of WRPROF to ECBRATE

-.012

-.008

-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20 22 24



W&R trading companies’ profit meet an increase in the first 3 quarters after a monetary

policy tightening measure and return back to trend in about two years. Again, this result is

economically viable; when interest rates are high, the customers prefer to have a better

management of the stocks and reduce costs, so they go to wholesale companies to resupply.

Germany - manufacturing industry:

53


.015

.020

.025

.030

.035

.040

.045

.050

.25

.30

.35

.40

.45

.50

.55

.60

2000 2001 2002 2003 2004 2005 2006

MANCOV ECBRATE

As it can be seen in the graph above, the influence of the ECB rate on the coverage ratio is

significantly lagged for the manufacturing companies in Germany. Another interesting fact is that the

coverage ratio reacts at a smaller lag when the interest rate increases and at a greater lag when it

decreases - quite usual for the banking sector to react slower when it comes to decreases in interest

rates for loans.

-.04

-.03

-.02

-.01

.00

.01

2 4 6 8 10 12 14 16 18 20 22 24


-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20 22 24


-.20

-.15

-.10

-.05

.00

.05

.10

2 4 6 8 10 12 14 16 18 20 22 24


-.015

-.010

-.005

.000

.005

.010

.015

2 4 6 8 10 12 14 16 18 20 22 24



As can be seen in the graphs above, the employees expenses decrease in the first 2 years

after a monetary policy tightening measure, to return back to trend after 4 years. The difference

between Germany and Portugal might come from the fact that Germany is a more tech country,

using less human workforce and usually, people have technical background, so the companies can

afford cost cuts. Another reason could stay in the way the indicator is calculated (as % of revenues),

meaning that the expenses cut is lower than the relatively decrease in sales.

-.2

-.1

.0

.1

.2

.3

2 4 6 8 10 12 14 16 18 20 22 24


-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20 22 24


-.6

-.4

-.2

.0

.2

.4

.6

.8

2 4 6 8 10 12 14 16 18 20 22 24


-.03

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20 22 24



The interest expenses of the German manufacturing companies increase in the first 2 years

after a monetary policy tightening measure, to return back to trend in about 7 years.

54


-.008

-.004

.000

.004

.008

.012

.016

.020

.024

2 4 6 8 10 12 14 16 18 20 22 24


-.002

.000

.002

.004

.006

.008

.010

.012

2 4 6 8 10 12 14 16 18 20 22 24


-.3

-.2

-.1

.0

.1

.2

.3

.4

2 4 6 8 10 12 14 16 18 20 22 24


-.010

-.005

.000

.005

.010

.015

.020

2 4 6 8 10 12 14 16 18 20 22 24



Surprisingly, the profit of the German manufacturing companies increase until the second

year after a monetary policy tightening measure. Although the increase is not relatively high and it

ends in the 3rd year, a possible explanation is that high interest rates could determine them to

increase the price (given the technological specific of the industry) and to initiate cost reduction

programs.

Germany - W&R trading companies:

.015

.020

.025

.030

.035

.040

.045

.050

.24

.28

.32

.36

.40

.44

.48

.52

2000 2001 2002 2003 2004 2005 2006

WRCOV ECBRATE

As in the case of Portugal, the coverage ratio of the W&R trading companies in Germany

closely follows the movements in the interest rate, with roughly 1 year lag.

.015

.020

.025

.030

.035

.040

.045

.050

-.6

-.4

-.2

.0

.2

.4

.6

.8

2000 2001 2002 2003 2004 2005 2006

WRPROF ECBRATE

Given the nature of the data and the few numbers of observations, it was not possible to

build a VAR model for the German W&R trading companies. Looking at the graph, it can be seen that

the correlation between the ECB rate and the profits is low (computing correlograms and cross

correlograms in Eviews it can be seen that the correlation is inexistent). However, Germany has also

a competitive advantage in the W&R trading, therefore one can say that the profitability of the

companies in the area follows a rather seasonal or even technological pattern.

55


-.07

-.06

-.05

-.04

-.03

-.02

-.01

.00

.01

2 4 6 8 10 12 14 16 18 20 22 24


-.008

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20 22 24


-.3

-.2

-.1

.0

.1

.2

2 4 6 8 10 12 14 16 18 20 22 24


-.020

-.015

-.010

-.005

.000

.005

.010

.015

2 4 6 8 10 12 14 16 18 20 22 24



The interest expenses of the German W&R trading companies decrease in periods of

monetary tightening until the 2nd year, to return back to trend in the 5 th year. In periods with high

interest rates, the trading companies could approach the trade credit method of financing instead of

going to the banking sector. Moreover, unlike Portugal, the German banking system is more implied

in the industry, therefore the companies here could have an competitive advantage with regards to

financing.

-.016

-.012

-.008

-.004

.000

.004

2 4 6 8 10 12 14 16 18 20 22 24


-.002

.000

.002

.004

.006

.008

.010

.012

2 4 6 8 10 12 14 16 18 20 22 24


-.20

-.16

-.12

-.08

-.04

.00

.04

.08

.12

.16

2 4 6 8 10 12 14 16 18 20 22 24


-.015

-.010

-.005

.000

.005

.010

.015

.020

2 4 6 8 10 12 14 16 18 20 22 24



Similar to the German manufacturing companies and the Portuguese W&R trading

companies, the employee benefits in the German companies decrease until the 2 nd year after a

monetary policy tightening measure, to return back to trend in about 6 years.

Italy - manufacturing industry:

.025

.030

.035

.040

.045

.050

.055

.060

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1999 2000 2001 2002 2003 2004 2005 2006 2007

MANCOV ECBRATE

As in the previous cases, the coverage ratio of the Italian manufacturing companies follows

closely the ECB rate, with 1 year lag.

56


-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20 22 24


-.002

.000

.002

.004

.006

.008

.010

.012

2 4 6 8 10 12 14 16 18 20 22 24


-.06

-.04

-.02

.00

.02

.04

.06

2 4 6 8 10 12 14 16 18 20 22 24


-.012

-.008

-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20 22 24



As can be seen above, the employee benefits expenses increase until the 2nd year after a

monetary policy tightening, returning back to trend in about 5 years. A possible reason (also valid in

the cases of Portugal and Germany) is that monetary tightening measures are taken to avoid an asset

bubble, usually covered by a significant economic growth, characterized, among others, by an

increase in salaries.

-.05

-.04

-.03

-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20 22 24


-.003

-.002

-.001

.000

.001

.002

.003

.004

.005

.006

2 4 6 8 10 12 14 16 18 20 22 24


-.12

-.08

-.04

.00

.04

.08

.12

2 4 6 8 10 12 14 16 18 20 22 24


-.008

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20 22 24



The interest rate expenses of the Italian manufacturing companies increase a relatively small

amount in the first 2 years after a monetary policy tightening measure, to afterwards decrease until

the 6th year when they return to the level. This might suggest a financing reorientation of the

companies or simply a reduction of loans caused by a reduction in activity121.

-.25

-.20

-.15

-.10

-.05

.00

.05

2 4 6 8 10 12 14 16 18 20 22 24


-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20 22 24


-.8

-.4

.0

.4

2 4 6 8 10 12 14 16 18 20 22 24


-.015

-.010

-.005

.000

.005

.010

.015

2 4 6 8 10 12 14 16 18 20 22 24



The manufacturing companies meet a decrease in profits until the 2nd year after a monetary

policy tightening measure, but returning to trend in the 4 th year. Given the low technological

advantage that Italy has, the model might offer the right development.

121 According to wikipedia, „Italy suffers from structural weaknesses due to its geographical conformation and the lack of raw materials and energy resources, while the Italian economy is weakened by the lack of infrastructure development, market reforms and research investment”. A reduction of manufacturing activity is therefore possible both in periods with high interest rates and low demand, as there is at the moment due to the financial crisis.

57


Italy - W&R tranding:

-.2

-.1

.0

.1

.2

.3

2 4 6 8 10 12 14 16 18 20 22 24

Response of WRPROF to NonfactorizedOne S.D. ECBRATE Innovation

-.003

-.002

-.001

.000

.001

.002

.003

.004

.005

2 4 6 8 10 12 14 16 18 20 22 24


-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12 14 16 18 20 22 24

Response of WRPROF to ECBRATE

-.008

-.004

.000

.004

.008

2 4 6 8 10 12 14 16 18 20 22 24



The wholesale & retail companies’ profitability increases in the first 2-3 years after a

monetary policy measure and oscillating afterwards. This can be motivated both by the area specifics

and the way this index is calculated (an increase in profits relatively to the revenues might mean a

higher decrease in revenues and a relatively lower decrease in profits).

-.06

-.04

-.02

.00

.02

.04

2 4 6 8 10 12 14 16 18 20 22 24


-.006

-.004

-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20 22 24


-.10

-.05

.00

.05

.10

2 4 6 8 10 12 14 16 18 20 22 24


-.015

-.010

-.005

.000

.005

.010

.015

2 4 6 8 10 12 14 16 18 20 22 24



The employee benefits decrease for 2 years after a monetary policy tightening measure for

the W&R trading companies, similar to Portugal and Germany. In this case as well, the movement in

this indicator can also mean that the employee benefits remained slightly unchanged, while the

revenues decreased.

-.04

-.03

-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10


-.004

-.003

-.002

-.001

.000

.001

.002

.003

1 2 3 4 5 6 7 8 9 10


-.08

-.06

-.04

-.02

.00

.02

.04

.06

1 2 3 4 5 6 7 8 9 10


-.006

-.004

-.002

.000

.002

.004

.006

1 2 3 4 5 6 7 8 9 10



The interest expenses of the Italian W&R trading companies tend to decrease after a

monetary policy tightening measure until aprox. the 4 th year and return back to trend in about 7

years. Similar companies in Portugal and Germany registered the same trend.

58


Conclusions

In order to maintain a healthy economic activity a sine qua non condition is price stability.

Central banks around the world have the important mission to assure a long term stability. However,

the last century proved that instability was actually quite common.

Credit driven asset bubbles, moral hazard, weak banking regulation, herd mentality,

unsustainable growth and irrational government spending were factors that constantly shook the

world economic environment. Still, in time, monetary policy measures have proven their efficiency in

solving these problems, but frequent financial crises prove that no efficient measures had been taken

in order to prevent them.

59


Empirical evidence shows that monetary policy measures have effect upon output, prices,

aggregate demand, inventories, private consumption (durable and non durable products) and

residential investment, while companies are affected in decisions regarding business investments,

expenses and cash flow management. Although the monetary policy is conducted in a sustained way,

countries within the Euro Area react differently, depending on their level of development and

financial structure.

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Working papers and websites:

Europa Glossary, European Central Bank(http://europa.eu/scadplus/glossary/european_central_bank_en.htm)

„Measuring inflation – the Harmonised Index of Consumer Prices (HICP)”, European Central Bank http://www.ecb.int/stats/prices/hicp/html/index.en.html

General Documentation „The Implementation Of Monetary Policy In The Euro Area” November 2008, © European Central Bank

Emerging Market Trade Association survey, quoted by Wikipedia.

Bureau of Economic Analysis Press Release (US)

http://www.bea.gov/newsreleases/national/gdp/gdpnewsrelease.htm)

Bureau of Labor Statistics, United States Department of Labor, Labor Force Statistics from the Current Population Survey, extracted on 14th December 2009.

(http://data.bls.gov/PDQ/servlet/SurveyOutputServlet?data_tool=latest_numbers&series_id=LNU04000000&years_option=all_years&periods_option=specific_periods&periods=Annual+Data)

Gallup survey (http://www.gallup.com/poll/123824/Europe-Gov-Intervention-Financial-Crises.aspx)

ECB • Monthly Bulletin • October 2002

Bank for International Settlements, "Financial Structure and the Monetary Policy Transmission Mechanism", C.B. 394.

Grande Enciclopédia Universal, p. 10543, "Portugal", para. 4

Investing in Portugal Report, Financial Times

(Portuguese) PESSOA, M.F.; MENDES, B.; OLIVEIRA, J.S. CULTURAS MARINHAS EM PORTUGAL

Rank Order – GDP (purchasing power parity) CIA Factbook 2005. Retrieved 31 December 2006.

CIA Factbook". https://www.cia.gov/library/publications/the-world-factbook/geos/gm.html. Retrieved 2009-08-02.

CIA - The World Factbook - Country Comparison :: Exports". Cia.gov.

https://www.cia.gov/library/publications/the-world-factbook/rankorder/2078rank.html. Retrieved 2009-10-27.

GDP Sector composition: Field Listing - GDP composition by sector - United Nations

"Faostat". Faostat.fao.org. 2009-06-23.

64



http://data.bls.gov/PDQ/servlet/SurveyOutputServlet?data_tool=latest_numbers&series_id=LNU04000000&years_option=all_years&periods_option=specific_periods&periods=Annual+Data



http://www.bea.gov/newsreleases/national/gdp/gdpnewsrelease.htm

http://europa.eu/scadplus/glossary/european_central_bank_en.htm


http://faostat.fao.org/site/567/DesktopDefault.aspx?PageID=567#ancor. Retrieved 2009-10-27

www.wikipedia.com/Italy

www.wikipedia.com/Germany

www.wikipedia.com/Portugal

www.wikipedia.com/Financial_crisis

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http://epp.eurostat.ec.europa.eu/portal/page/portal/eurostat/home/

Datastream database

BACH Database

Table of Contents

Chapter 1. The monetary policy of the European Central bank............................................................2

Introduction.......................................................................................................................................2

Basic notions..................................................................................................................................2

The ECB’s Monetary Policy.................................................................................................................4

The transmission mechanism.............................................................................................................5

Monetary policy instruments.............................................................................................................8

Open market operations................................................................................................................8

Standing facilities..........................................................................................................................10

Minimum reserves requirements.................................................................................................10

Chapter 2. Financial Crises Around the World......................................................................................11

Financial Crises in Theory.................................................................................................................11

The 20th Century’s Financial Crises..................................................................................................14

The Global Financial Crisis of 2007 - 2009(+)......................................................................................22

65

http://www.ecb.int/

http://www.wikipedia.com/Financial_crisis

http://www.wikipedia.com/Portugal

http://www.wikipedia.com/Germany

http://www.wikipedia.com/Italy


Conclusions......................................................................................................................................26

Chapter 3. Empirical evidence of the monetary policy effects in Europe.............................................28

4.1. Economy response to a policy shock (VAR evidence)................................................................31

The models.......................................................................................................................................32

Model 1 --- GDP, GDP deflator, Commodity index, ECB rate........................................................33

Model 2 --- Demand growth, inventories growth, GDP deflator, Commodity index, ECB rate.....39

Model 3 --- durables consumption, nondurable consumption, residential investment, GDP deflator, Commodity index, ECB rate...........................................................................................42

4.2. The Credit Channel of the Monetary Policy Transmission.........................................................47

Introduction..................................................................................................................................47

Empirical research........................................................................................................................49

Conclusions..........................................................................................................................................60

References............................................................................................................................................61

APPENDICES.........................................................................................................................................69

Appendix 1 ---- LS 1 1 dlgdp dldefl dlcmdty ECBrate @ c..............................................................69

Appendix 2 ---- LS 1 1 dlgdp dldefl dlcmdty ECBrate @ c..............................................................76

Appendix 3 ---- LS 1 1 inddur, innondur, rescostindex, dldefl dlcmdty ECBrate @ c.....................84

Appendix 4 ---- Descriptive statistics for VAR variables.....................................................................4

Appendix 5 ---- Autocorrelations of the error terms in the VAR models...........................................5

Appendix 6 --- The Credit Channel of the Monetary Policy Transmission --- VAR models..................8

Appendix 7 ---- VAR models in theory.............................................................................................10

Method - Shortly about VAR.........................................................................................................10

(Dis)Advantages of the VAR models.............................................................................................11

Causality tests...............................................................................................................................12

66


67


APPENDICES

Appendix 1 ---- LS 1 1 dlgdp dldefl dlcmdty ECBrate @ c

VAR Lag Order Selection CriteriaEndogenous variables: DLGDP DLDEFL DLCMDTY ECBRATE Exogenous variables: C Date: 12/16/09 Time: 00:34Sample: 1999Q1 2009Q2Included observations: 39

Lag LogL LR FPE AIC SC HQ

0 304.1036 NA 2.43e-12 -15.38993 -15.21931 -15.328711 417.5345 197.7770* 1.66e-14* -20.38639* -19.53328* -20.08030*2 428.6671 17.12696 2.19e-14 -20.13677 -18.60118 -19.585813 439.9203 15.00433 3.00e-14 -19.89335 -17.67527 -19.09752

* indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion

VAR Lag Exclusion Wald TestsDate: 12/16/09 Time: 00:44Sample: 1999Q1 2009Q2Included observations: 40

Chi-squared test statistics for lag exclusion:Numbers in [ ] are p-values

DLGDP DLDEFL DLCMDTY ECBRATE Joint

Lag 1 13.89750 31.65341 1.673388 56.28811 143.5833[ 0.007629] [ 2.25e-06] [ 0.795546] [ 1.74e-11] [ 0.000000]

Lag 2 5.851497 1.334777 6.301758 8.362670 16.92244[ 0.210516] [ 0.855448] [ 0.177718] [ 0.079161] [ 0.390633]

df 4 4 4 4 16

68


Table Granger 1:

Table Granger 2:

69

Pairwise Granger Causality TestsSample: 1999Q1 2009Q2Lags: 1

Null Hypothesis: Obs F-Statistic Probability

DLDEFL does not Granger Cause DLGDP 41 2.82116 0.10123 DLGDP does not Granger Cause DLDEFL 11.6456 0.00154

DLCMDTY does not Granger Cause DLGDP 41 1.32808 0.25635 DLGDP does not Granger Cause DLCMDTY 3.1E-05 0.99556

ECBRATE does not Granger Cause DLGDP 41 6.12648 0.01789 DLGDP does not Granger Cause ECBRATE 26.4385 8.5E-06

DLCMDTY does not Granger Cause DLDEFL 41 1.66376 0.20489 DLDEFL does not Granger Cause DLCMDTY 0.03777 0.84693

ECBRATE does not Granger Cause DLDEFL 41 12.0387 0.00131 DLDEFL does not Granger Cause ECBRATE 7.72357 0.00843

ECBRATE does not Granger Cause DLCMDTY 41 4.31441 0.04460 DLCMDTY does not Granger Cause ECBRATE 8.28460 0.00653


VAR Granger Causality/Block Exogeneity Wald TestsSample: 1999Q1 2009Q2Included observations: 41

Dependent variable: DLGDP

Excluded Chi-sq df Prob.

DLDEFL 3.089083 1 0.0788DLCMDTY 2.655725 1 0.1032ECBRATE 3.881121 1 0.0488

All 10.80447 3 0.0128

Dependent variable: DLDEFL


DLGDP 6.084005 1 0.0136DLCMDTY 0.175480 1 0.6753ECBRATE 7.623185 1 0.0058

All 21.84628 3 0.0001

Dependent variable: DLCMDTY


DLGDP 0.568317 1 0.4509DLDEFL 0.581734 1 0.4456

ECBRATE 4.873824 1 0.0273

All 4.925909 3 0.1773

Dependent variable: ECBRATE


DLGDP 6.576587 1 0.0103DLDEFL 2.424562 1 0.1194

DLCMDTY 1.534459 1 0.2154

All 30.13719 3 0.0000

Vector Autoregression Estimates Sample (adjusted): 1999Q2 2009Q2 Included observations: 41 after adjustments Standard errors in ( ) & t-statistics in [ ]

DLGDP DLDEFL DLCMDTY ECBRATE

DLGDP(-1) 0.532012 0.172548 1.506529 28.23878 (0.15008) (0.06995) (1.99840) (11.0115)[ 3.54489] [ 2.46658] [ 0.75387] [ 2.56449]

70


DLDEFL(-1) -0.316456 0.947714 1.828621 -20.57035 (0.18005) (0.08393) (2.39752) (13.2107)[-1.75758] [ 11.2923] [ 0.76272] [-1.55710]

DLCMDTY(-1) 0.022867 -0.002740 0.264560 1.275345 (0.01403) (0.00654) (0.18685) (1.02956)[ 1.62964] [-0.41890] [ 1.41592] [ 1.23873]

ECBRATE(-1) -0.001639 0.001071 -0.024453 0.911812 (0.00083) (0.00039) (0.01108) (0.06103)[-1.97006] [ 2.76101] [-2.20767] [ 14.9398]

C 0.012681 -0.002938 0.039747 0.512613 (0.00411) (0.00192) (0.05475) (0.30167)[ 3.08423] [-1.53318] [ 0.72601] [ 1.69927]

R-squared 0.599669 0.827634 0.217708 0.892272 Adj. R-squared 0.555188 0.808483 0.130787 0.880302 Sum sq. resids 0.000777 0.000169 0.137707 4.181020 S.E. equation 0.004645 0.002165 0.061848 0.340792 F-statistic 13.48138 43.21459 2.504662 74.54392 Log likelihood 164.7423 196.0381 58.59564 -11.37463 Akaike AIC -7.792307 -9.318934 -2.614422 0.798763 Schwarz SC -7.583334 -9.109962 -2.405449 1.007735 Mean dependent 0.003589 0.019976 0.009864 3.006098 S.D. dependent 0.006964 0.004947 0.066338 0.985025

Determinant resid covariance (dof adj.) 1.45E-14 Determinant resid covariance 8.63E-15 Log likelihood 431.1665 Akaike information criterion -20.05690 Schwarz criterion -19.22101

The information employed by the model can be considered to be at an acceptable level, i.e. the independent variables explain quite well the dependent variables.

VAR Residual Heteroskedasticity Tests: No Cross Terms (only levels and squares)Sample: 1999Q1 2009Q2Included observations: 41

Joint test:

Chi-sq df Prob.

99.58156 80 0.0683

71


Individual components:

Dependent R-squared F(8,32) Prob. Chi-sq(8) Prob.

res1*res1 0.430436 3.022911 0.0120 17.64786 0.0240res2*res2 0.197704 0.985693 0.4651 8.105874 0.4232res3*res3 0.185930 0.913583 0.5179 7.623134 0.4711res4*res4 0.281610 1.568004 0.1737 11.54600 0.1726res2*res1 0.264237 1.436530 0.2196 10.83370 0.2113res3*res1 0.196845 0.980360 0.4689 8.070656 0.4266res3*res2 0.230506 1.198222 0.3310 9.450753 0.3057res4*res1 0.192885 0.955925 0.4865 7.908299 0.4425res4*res2 0.104436 0.466457 0.8704 4.281857 0.8308res4*res3 0.189532 0.935423 0.5016 7.770829 0.4562

VAR Residual Heteroskedasticity Tests: Includes Cross TermsDate: 12/17/09 Time: 03:35Sample: 1999Q1 2009Q2Included observations: 41

Joint test:

Chi-sq df Prob.

72


148.7738 140 0.2900



res1*res1 0.568506 2.446842 0.0235 23.30875 0.0554res2*res2 0.288219 0.752008 0.7066 11.81700 0.6210res3*res3 0.394923 1.212124 0.3242 16.19185 0.3018res4*res4 0.531892 2.110195 0.0483 21.80757 0.0827res2*res1 0.362045 1.053946 0.4372 14.84385 0.3889res3*res1 0.420699 1.348694 0.2465 17.24868 0.2432res3*res2 0.250630 0.621128 0.8234 10.27581 0.7418res4*res1 0.374868 1.113658 0.3916 15.36959 0.3534res4*res2 0.119432 0.251886 0.9953 4.896715 0.9872res4*res3 0.402113 1.249032 0.3015 16.48662 0.2846

VAR stability test

Root Modulus

0.803062 - 0.333352i 0.869501 0.803062 + 0.333352i 0.869501 0.852481 0.852481 0.197493 0.197493

No root lies outside the unit circle. VAR satisfies the stability condition.

VAR Residual Normality TestsOrthogonalization: Cholesky (Lutkepohl)H0: residuals are multivariate normalSample: 1999Q1 2009Q2Included observations: 41

Component Skewness Chi-sq df Prob.

1 -0.716507 3.508113 1 0.06112 -0.171260 0.200423 1 0.6544

73


3 -0.407191 1.133000 1 0.28714 0.333917 0.761919 1 0.3827

Joint 5.603455 4 0.2308

Component Kurtosis Chi-sq df Prob.

1 3.689690 0.812606 1 0.36742 2.278275 0.889848 1 0.34553 2.421712 0.571296 1 0.44974 2.788018 0.076766 1 0.7817

Joint 2.350516 4 0.6716

Component Jarque-Bera df Prob.

1 4.320719 2 0.11532 1.090270 2 0.57983 1.704296 2 0.42654 0.838685 2 0.6575

Joint 7.953971 8 0.4380

Var Residual Serial Correlation LM TestsH0: no serial correlation at lag h

Lags LM-Stat Prob

1 13.52536 0.63402 19.30794 0.25303 15.11407 0.51634 13.75871 0.61675 13.70011 0.6210

74


6 10.28195 0.85157 12.79672 0.68768 23.53572 0.10019 9.460202 0.8933

10 15.02753 0.522611 11.69185 0.764912 12.55184 0.7052

Probs from chi-square with 16 df.

Covariance matrix of the residuals:

DLGDP DLDEFL DLCMDTY ECBRATEDLGDP 2.16E-05 -1.40E-06 0.000155 0.000865DLDEFL -1.40E-06 4.69E-06 -4.24E-06 -6.23E-05

DLCMDTY 0.000155 -4.24E-06 0.003825 0.014788ECBRATE 0.000865 -6.23E-05 0.014788 0.116139

Appendix 2 ---- LS 1 1 dlgdp dldefl dlcmdty ECBrate @ c

VAR Lag Order Selection CriteriaEndogenous variables: DEMGR INVGR DLDEFL DLCMDTY ECBRATE Exogenous variables: C Sample: 1999Q1 2009Q2Included observations: 39


75


0 479.8263 NA 1.83e-17 -24.35007 -24.13679 -24.273541 592.8002 191.1867* 2.03e-19* -28.86155* -27.58189* -28.40242*2 617.4324 35.36934 2.22e-19 -28.84269 -26.49664 -28.000953 636.8684 22.92450 3.52e-19 -28.55735 -25.14492 -27.33300

* indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion

VAR Lag Exclusion Wald TestsDate: 12/16/09 Time: 02:48Sample: 1999Q1 2009Q2Included observations: 40

Chi-squared test statistics for lag exclusion:Numbers in [ ] are p-values

DEMGR INVGR DLDEFL DLCMDTY ECBRATE Joint

Lag 1 33.57020 17.55657 33.75856 4.090335 77.28465 181.0441[ 2.90e-06] [ 0.003557] [ 2.66e-06] [ 0.536485] [ 3.11e-15] [ 0.000000]

Lag 2 21.26144 5.620745 3.772836 9.360358 7.154178 42.58018[ 0.000723] [ 0.344887] [ 0.582563] [ 0.095526] [ 0.209424] [ 0.015556]

df 5 5 5 5 5 25

Table 1 - Pairwise Granger Causality Tests

76


Table 2

VAR Granger Causality/Block Exogeneity Wald Tests

77

Sample: 1999Q1 2009Q2Lags: 1


INVGR does not Granger Cause DEMGR 41 13.1404 0.00084 DEMGR does not Granger Cause INVGR 5.30275 0.02686

DLDEFL does not Granger Cause DEMGR 41 2.27436 0.13980 DEMGR does not Granger Cause DLDEFL 5.02725 0.03087

DLCMDTY does not Granger Cause DEMGR 41 7.78894 0.00818 DEMGR does not Granger Cause DLCMDTY 0.74625 0.39309

ECBRATE does not Granger Cause DEMGR 41 1.13751 0.29291 DEMGR does not Granger Cause ECBRATE 6.86343 0.01257

DLDEFL does not Granger Cause INVGR 41 0.14466 0.70581 INVGR does not Granger Cause DLDEFL 1.20423 0.27938

DLCMDTY does not Granger Cause INVGR 41 6.88178 0.01247 INVGR does not Granger Cause DLCMDTY 1.90332 0.17577

ECBRATE does not Granger Cause INVGR 41 0.14034 0.71003 INVGR does not Granger Cause ECBRATE 0.26190 0.61178





Sample: 1999Q1 2009Q2Included observations: 41

Dependent variable: DEMGR


INVGR 11.52133 1 0.0007DLDEFL 4.060150 1 0.0439

DLCMDTY 10.48064 1 0.0012ECBRATE 1.135986 1 0.2865

All 31.11100 4 0.0000

Dependent variable: INVGR


DEMGR 1.268016 1 0.2601DLDEFL 0.000185 1 0.9892

DLCMDTY 2.786248 1 0.0951ECBRATE 0.023047 1 0.8793

All 8.262516 4 0.0824



DEMGR 1.341644 1 0.2467INVGR 0.045844 1 0.8305

DLCMDTY 0.185765 1 0.6665ECBRATE 10.00162 1 0.0016

All 16.07702 4 0.0029



DEMGR 0.007081 1 0.9329INVGR 1.326139 1 0.2495DLDEFL 0.107664 1 0.7428

ECBRATE 3.988841 1 0.0458All 6.002579 4 0.1990



DEMGR 1.554857 1 0.2124INVGR 3.000132 1 0.0833DLDEFL 6.260283 1 0.0123

DLCMDTY 5.642861 1 0.0175

All 24.34304 4 0.0001

Roots of Characteristic Polynomial

Root Modulus

0.804581 - 0.214210i 0.832608 0.804581 + 0.214210i 0.832608 0.510755 0.510755-0.353032 0.353032 0.321854 0.321854


VAR Residual Heteroskedasticity Tests: No Cross Terms (only levels and squares)Sample: 1999Q1 2009Q2Included observations: 41

78


Joint test:

Chi-sq df Prob.

152.2424 150 0.4336



res1*res1 0.318286 1.400670 0.2275 13.04971 0.2209res2*res2 0.327468 1.460752 0.2027 13.42617 0.2008res3*res3 0.256715 1.036136 0.4385 10.52531 0.3957res4*res4 0.220405 0.848150 0.5883 9.036593 0.5286res5*res5 0.249644 0.998102 0.4669 10.23540 0.4201res2*res1 0.256135 1.032987 0.4408 10.50152 0.3976res3*res1 0.547289 3.626747 0.0030 22.43886 0.0130res3*res2 0.344551 1.577012 0.1617 14.12657 0.1673res4*res1 0.217776 0.835218 0.5994 8.928814 0.5389res4*res2 0.287494 1.210494 0.3238 11.78727 0.2995res4*res3 0.185939 0.685227 0.7296 7.623493 0.6656res5*res1 0.270101 1.110160 0.3867 11.07415 0.3518res5*res2 0.223483 0.863404 0.5754 9.162789 0.5167res5*res3 0.204830 0.772776 0.6534 8.398010 0.5900res5*res4 0.207173 0.783927 0.6437 8.494087 0.5807

VAR Residual Heteroskedasticity Tests: Includes Cross TermsDate: 12/17/09 Time: 03:54Sample: 1999Q1 2009Q2

79


Included observations: 41

Joint test:

Chi-sq df Prob.

313.1178 300 0.2894



res1*res1 0.606166 1.539141 0.1714 24.85281 0.2071res2*res2 0.485865 0.945013 0.5497 19.92045 0.4629res3*res3 0.390732 0.641314 0.8357 16.02002 0.7154res4*res4 0.479006 0.919409 0.5736 19.63926 0.4807res5*res5 0.541417 1.180629 0.3570 22.19808 0.3299res2*res1 0.609310 1.559575 0.1642 24.98172 0.2021res3*res1 0.707189 2.415176 0.0276 28.99476 0.0879res3*res2 0.554378 1.244054 0.3150 22.72949 0.3023res4*res1 0.429900 0.754079 0.7331 17.62591 0.6120res4*res2 0.635734 1.745245 0.1108 26.06509 0.1637res4*res3 0.366074 0.577470 0.8859 15.00902 0.7759res5*res1 0.485203 0.942512 0.5520 19.89331 0.4646res5*res2 0.601430 1.508972 0.1826 24.65865 0.2148res5*res3 0.480334 0.924313 0.5690 19.69370 0.4772res5*res4 0.485901 0.945152 0.5496 19.92195 0.4628

Vector Autoregression Estimates Sample (adjusted): 1999Q2 2009Q2 Included observations: 41 after adjustments

80


Standard errors in ( ) & t-statistics in [ ]

DEMGR INVGR DLDEFL DLCMDTY ECBRATE

DEMGR(-1) 0.413687 0.138212 0.107331 0.209112 18.04249 (0.15733) (0.12274) (0.09266) (2.48510) (14.4694)[ 2.62943] [ 1.12606] [ 1.15829] [ 0.08415] [ 1.24694]

INVGR(-1) -0.737055 -0.525420 0.027384 -3.949805 -34.59069 (0.21714) (0.16940) (0.12789) (3.42990) (19.9705)[-3.39431] [-3.10159] [ 0.21411] [-1.15158] [-1.73209]

DLDEFL(-1) -0.287298 -0.001511 0.885283 0.738973 -32.80948 (0.14258) (0.11123) (0.08398) (2.25213) (13.1130)[-2.01498] [-0.01359] [ 10.5420] [ 0.32812] [-2.50206]

DLCMDTY(-1) 0.035307 0.014202 0.002769 0.364719 2.382649 (0.01091) (0.00851) (0.00642) (0.17227) (1.00302)[ 3.23738] [ 1.66921] [ 0.43100] [ 2.11717] [ 2.37547]

ECBRATE(-1) -0.000723 8.03E-05 0.001263 -0.021395 0.950470 (0.00068) (0.00053) (0.00040) (0.01071) (0.06237)[-1.06583] [ 0.15181] [ 3.16253] [-1.99721] [ 15.2382]

C 0.009110 -0.001333 -0.002031 0.055656 0.668475 (0.00347) (0.00271) (0.00205) (0.05486) (0.31940)[ 2.62308] [-0.49208] [-0.99295] [ 1.01457] [ 2.09290]

R-squared 0.580535 0.300669 0.810213 0.240861 0.883274 Adj. R-squared 0.520611 0.200765 0.783101 0.132412 0.866598 Sum sq. resids 0.000536 0.000326 0.000186 0.133631 4.530266 S.E. equation 0.003912 0.003052 0.002304 0.061790 0.359772 F-statistic 9.687916 3.009571 29.88349 2.220972 52.96928 Log likelihood 172.3601 182.5397 194.0643 59.21151 -13.01926 Akaike AIC -8.115129 -8.611690 -9.173870 -2.595684 0.927769 Schwarz SC -7.864362 -8.360924 -8.923103 -2.344917 1.178535 Mean dependent 0.003066 -0.000398 0.019976 0.009864 3.006098 S.D. dependent 0.005650 0.003414 0.004947 0.066338 0.985025


The information employed by the model can be considered to be at an acceptable level, i.e. the independent variables explain quite well the dependent variables.

81


VAR Residual Normality TestsOrthogonalization: Cholesky (Lutkepohl)H0: residuals are multivariate normalSample: 1999Q1 2009Q2Included observations: 41


1 -0.220724 0.332915 1 0.56392 0.099022 0.067003 1 0.79583 0.012198 0.001017 1 0.97464 -0.669182 3.060000 1 0.08025 0.025696 0.004512 1 0.9464

Joint 3.465446 5 0.6286


1 1.846957 2.271245 1 0.13182 1.955002 1.865534 1 0.17203 2.022616 1.631936 1 0.20144 2.966927 0.001869 1 0.96555 1.797829 2.468911 1 0.1161

Joint 8.239494 5 0.1435


1 2.604160 2 0.27202 1.932537 2 0.38053 1.632953 2 0.44204 3.061868 2 0.21635 2.473423 2 0.2903

Joint 11.70494 10 0.3053

VAR Residual Serial Correlation LM TestsH0: no serial correlation at lag order hSample: 1999Q1 2009Q2Included observations: 41

82


Lags LM-Stat Prob

1 26.55999 0.37822 28.96505 0.26543 24.61474 0.48414 21.79795 0.64745 23.38740 0.55506 39.71997 0.03127 18.68471 0.81208 31.84909 0.16249 17.52933 0.8617

10 23.60132 0.542511 16.02916 0.914012 18.24155 0.8320

Probs from chi-square with 25 df.

Covariance matrix of the residuals

DEMGR INVGR DLDEFL DLCMDTY ECBRATEDEMGR 1.53E-05 3.85E-06 2.06E-06 7.23E-05 0.000537INVGR 3.85E-06 9.31E-06 4.97E-07 -2.40E-05 -3.81E-05

DLDEFL 2.06E-06 4.97E-07 5.31E-06 7.05E-06 6.25E-05DLCMDTY 7.23E-05 -2.40E-05 7.05E-06 0.003818 0.015267ECBRATE 0.000537 -3.81E-05 6.25E-05 0.015267 0.129436

Appendix 3 ---- LS 1 1 inddur, innondur, rescostindex, dldefl dlcmdty ECBrate @ c

83


Table 1

Pairwise Granger Causality TestsSample: 1999Q1 2009Q2Lags: 1


INDNONDUR does not Granger Cause INDDUR 41 0.72122 0.40106 INDDUR does not Granger Cause INDNONDUR 10.4236 0.00257

RESCOSTINDEX does not Granger Cause INDDUR 39 2.06413 0.15944 INDDUR does not Granger Cause RESCOSTINDEX 7.52246 0.00943

INVRATE does not Granger Cause INDDUR 39 3.08130 0.08770 INDDUR does not Granger Cause INVRATE 20.0809 7.2E-05

DLDEFL does not Granger Cause INDDUR 41 1.37439 0.24836 INDDUR does not Granger Cause DLDEFL 4.76942 0.03521

DLCMDTY does not Granger Cause INDDUR 41 0.00513 0.94327 INDDUR does not Granger Cause DLCMDTY 1.88030 0.17834

ECBRATE does not Granger Cause INDDUR 41 3.26016 0.07891 INDDUR does not Granger Cause ECBRATE 28.7209 4.3E-06

RESCOSTINDEX does not Granger Cause INDNONDUR 39 0.23991 0.62725 INDNONDUR does not Granger Cause RESCOSTINDEX 0.93580 0.33981

INVRATE does not Granger Cause INDNONDUR 39 6.69429 0.01386

84

VAR Lag Order Selection CriteriaEndogenous variables: INDDUR INDNONDUR RESCOSTINDEX INVRATE DLDEFL DLCMDTY ECBRATE Exogenous variables: C Date: 12/16/09 Time: 04:25Sample: 1999Q1 2009Q2Included observations: 36


0 628.0550 NA 2.44e-24 -34.50306 -34.19515 -34.395591 749.5801 189.0390* 4.58e-26* -38.53223 -36.06898* -37.67249*2 799.9363 58.74886 5.88e-26 -38.60757 -33.98897 -36.995563 855.6200 43.30959 1.12e-25 -38.97889* -32.20495 -36.61460

* indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion


INDNONDUR does not Granger Cause INVRATE 0.65330 0.42424

DLDEFL does not Granger Cause INDNONDUR 41 1.18166 0.28387 INDNONDUR does not Granger Cause DLDEFL 1.87009 0.17950

DLCMDTY does not Granger Cause INDNONDUR 41 2.75703 0.10506 INDNONDUR does not Granger Cause DLCMDTY 0.35752 0.55344

ECBRATE does not Granger Cause INDNONDUR 41 0.68838 0.41190 INDNONDUR does not Granger Cause ECBRATE 2.44747 0.12601

INVRATE does not Granger Cause RESCOSTINDEX 38 0.53151 0.47082 RESCOSTINDEX does not Granger Cause INVRATE 0.84528 0.36419

DLDEFL does not Granger Cause RESCOSTINDEX 39 0.72715 0.39945 RESCOSTINDEX does not Granger Cause DLDEFL 0.27077 0.60600

DLCMDTY does not Granger Cause RESCOSTINDEX 39 4.12984 0.04956 RESCOSTINDEX does not Granger Cause DLCMDTY 9.24188 0.00439

ECBRATE does not Granger Cause RESCOSTINDEX 39 0.63753 0.42984 RESCOSTINDEX does not Granger Cause ECBRATE 0.04975 0.82477

DLDEFL does not Granger Cause INVRATE 39 2.53363 0.12019 INVRATE does not Granger Cause DLDEFL 0.92306 0.34308

DLCMDTY does not Granger Cause INVRATE 39 1.46292 0.23435 INVRATE does not Granger Cause DLCMDTY 5.5E-05 0.99410

ECBRATE does not Granger Cause INVRATE 39 4.28803 0.04561 INVRATE does not Granger Cause ECBRATE 10.1764 0.00295




Table 2VAR Granger Causality/Block Exogeneity Wald TestsDate: 12/16/09 Time: 04:34

Sample: 1999Q1 2009Q2Included observations: 38

85


Dependent variable: INDDUR


INDNONDUR 1.660210 1 0.1976RESCOSTINDEX 3.431675 1 0.0640

INVRATE 0.514131 1 0.4734DLDEFL 0.824773 1 0.3638

DLCMDTY 0.004991 1 0.9437ECBRATE 3.089848 1 0.0788

All 11.10652 6 0.0851

Dependent variable: INDNONDUR


INDDUR 5.241219 1 0.0221RESCOSTINDEX 0.647310 1 0.4211

INVRATE 3.150076 1 0.0759DLDEFL 1.057438 1 0.3038

DLCMDTY 0.161097 1 0.6881ECBRATE 0.160510 1 0.6887

All 10.96945 6 0.0893

Dependent variable: RESCOSTINDEX


INDDUR 2.489519 1 0.1146INDNONDUR 0.026803 1 0.8700

INVRATE 0.038517 1 0.8444DLDEFL 0.035295 1 0.8510

DLCMDTY 0.928199 1 0.3353ECBRATE 0.074686 1 0.7846

All 8.168402 6 0.2260

Dependent variable: INVRATE



RESCOSTINDEX 0.227499 1 0.6334DLDEFL 0.044041 1 0.8338

DLCMDTY 0.074930 1 0.7843ECBRATE 4.058884 1 0.0439

All 26.15165 6 0.0002




RESCOSTINDEX 0.770769 1 0.3800INVRATE 0.462808 1 0.4963

DLCMDTY 0.030235 1 0.8620ECBRATE 6.630419 1 0.0100

All 10.39696 6 0.1089




RESCOSTINDEX 8.665820 1 0.0032INVRATE 0.995347 1 0.3184DLDEFL 2.453690 1 0.1172

ECBRATE 1.787638 1 0.1812

All 23.76681 6 0.0006



86



RESCOSTINDEX 0.041003 1 0.8395INVRATE 0.492415 1 0.4829DLDEFL 0.843186 1 0.3585

DLCMDTY 0.437931 1 0.5081

All 21.68621 6 0.0014

87

MSc. Finance and International Business Nicoleta Cristina Alexandru “The monetary policy and its effects on economy - an European view”

VAR Residual Heteroskedasticity Tests: No Cross Terms (only levels and squares)

Joint test:

Chi-sq df Prob.

419.4309 392 0.1632



res1*res1 0.645016 2.985119 0.0098 24.51061 0.0397res2*res2 0.516708 1.756448 0.1117 19.63490 0.1421res3*res3 0.352535 0.894511 0.5750 13.39633 0.4956res4*res4 0.833785 8.241082 0.0000 31.68384 0.0044res5*res5 0.418834 1.183972 0.3487 15.91569 0.3185res6*res6 0.608622 2.554768 0.0223 23.12764 0.0582res7*res7 0.818287 7.398103 0.0000 31.09492 0.0054res2*res1 0.540043 1.928908 0.0785 20.52165 0.1145res3*res1 0.631235 2.812170 0.0135 23.98694 0.0460res3*res2 0.444233 1.313160 0.2726 16.88085 0.2626res4*res1 0.672925 3.380011 0.0047 25.57113 0.0293res4*res2 0.535293 1.892393 0.0846 20.34112 0.1197res4*res3 0.786124 6.038514 0.0001 29.87273 0.0079res5*res1 0.392644 1.062077 0.4351 14.92049 0.3836res5*res2 0.757039 5.118951 0.0003 28.76747 0.0112res5*res3 0.389906 1.049937 0.4445 14.81644 0.3908res5*res4 0.396074 1.077438 0.4235 15.05081 0.3747res6*res1 0.610201 2.571768 0.0216 23.18763 0.0573res6*res2 0.422951 1.204142 0.3358 16.07215 0.3090res6*res3 0.680161 3.493658 0.0039 25.84612 0.0271res6*res4 0.830635 8.057257 0.0000 31.56414 0.0046res6*res5 0.307755 0.730373 0.7251 11.69468 0.6308res7*res1 0.686281 3.593856 0.0033 26.07867 0.0253res7*res2 0.417906 1.179464 0.3517 15.88042 0.3207res7*res3 0.817262 7.347355 0.0000 31.05594 0.0054res7*res4 0.853662 9.583596 0.0000 32.43915 0.0035res7*res5 0.327012 0.798282 0.6626 12.42646 0.5721res7*res6 0.787927 6.103810 0.0001 29.94123 0.0078

Taking the individual components we can observe that some of p-values reject the null hypothesis of homoskedasticity. However, the overall test confirms homoskedasticity and will be intrepreted as such.

0


VAR Residual Heteroskedasticity Tests: Includes Cross Terms:

Joint test:

Chi-sq df Prob.

1006.088 980 0.2745



res1*res1 0.871802 0.388595 0.9094 33.12847 0.5587res2*res2 0.908976 0.570636 0.8118 34.54109 0.4901res3*res3 0.960779 1.399820 0.5035 36.50962 0.3984res4*res4 0.983772 3.464072 0.2490 37.38333 0.3602res5*res5 0.969347 1.807056 0.4201 36.83519 0.3840res6*res6 0.954699 1.204251 0.5557 36.27855 0.4088res7*res7 0.975393 2.265110 0.3534 37.06495 0.3739res2*res1 0.987988 4.700121 0.1906 37.54356 0.3534res3*res1 0.996737 17.45386 0.0556 37.87600 0.3394res3*res2 0.895682 0.490633 0.8546 34.03592 0.5145res4*res1 0.964209 1.539409 0.4716 36.63993 0.3926res4*res2 0.961683 1.434174 0.4953 36.54395 0.3969res4*res3 0.998503 38.12042 0.0259 37.94312 0.3367res5*res1 0.885121 0.440277 0.8818 33.63462 0.5340res5*res2 0.999640 158.5027 0.0063 37.98631 0.3349res5*res3 0.965023 1.576561 0.4637 36.67086 0.3912res5*res4 0.989995 5.654486 0.1614 37.61982 0.3501res6*res1 0.914507 0.611246 0.7907 34.75125 0.4800res6*res2 0.866220 0.369997 0.9190 32.91635 0.5691res6*res3 0.984784 3.698402 0.2353 37.42181 0.3585res6*res4 0.980114 2.816329 0.2964 37.24432 0.3661res6*res5 0.944792 0.977904 0.6298 35.90210 0.4260res7*res1 0.903612 0.535698 0.8303 34.33725 0.4999res7*res2 0.926979 0.725414 0.7347 35.22521 0.4576res7*res3 0.998143 30.72197 0.0320 37.92945 0.3372res7*res4 0.985646 3.923858 0.2235 37.45455 0.3571res7*res5 0.768267 0.189446 0.9901 29.19413 0.7439res7*res6 0.954107 1.187995 0.5605 36.25607 0.4099

The VAR Residual Heteroskedasticity Tests wtih no Cross Terms is, however, stronger than the test including cross terms as it has smaller number of variables, loosing less degrees of freedom.

1


VAR stability test

Root Modulus

0.855646 - 0.217531i 0.882865 0.855646 + 0.217531i 0.882865 0.341677 - 0.543809i 0.642239 0.341677 + 0.543809i 0.642239-0.311888 0.311888-0.071747 - 0.096503i 0.120252-0.071747 + 0.096503i 0.120252


VAR Residual Serial Correlation LM TestsH0: no serial correlation at lag order h

Lags LM-Stat Prob

1 52.26490 0.34832 56.69381 0.21003 53.40298 0.30894 49.15234 0.46705 39.34930 0.83626 45.31386 0.62337 62.93759 0.08718 72.16916 0.01739 36.01859 0.9162

10 42.87299 0.718611 55.51690 0.242612 47.34367 0.5405

Covariance matrix of the residuals:

INDDUR INDNONDUR RESCOSTINDEX INVRATE DLDEFL DLCMDTY ECBRATEINDDUR 0.000306 6.11E-05 8.23E-06 5.38E-05 -8.97E-06 0.000456 0.003398

INDNONDUR 6.11E-05 8.37E-05 -2.12E-05 2.14E-07 -2.59E-06 -2.02E-05 0.000233RESCOSTINDEX 8.23E-06 -2.12E-05 4.20E-05 3.31E-05 3.00E-06 0.000164 0.000954

INVRATE 5.38E-05 2.14E-07 3.31E-05 0.000132 1.13E-06 0.000333 0.002327DLDEFL -8.97E-06 -2.59E-06 3.00E-06 1.13E-06 3.87E-06 -1.20E-05 -0.000102

DLCMDTY 0.000456 -2.02E-05 0.000164 0.000333 -1.20E-05 0.002612 0.012167

2


ECBRATE 0.003398 0.000233 0.000954 0.002327 -0.000102 0.012167 0.117166

VAR Residual Normality TestsH0: residuals are multivariate normalSample: 1999Q1 2009Q2Included observations: 38


1 0.133973 0.113675 1 0.73602 -0.999590 6.328135 1 0.01193 -0.164098 0.170545 1 0.67964 -0.134961 0.115357 1 0.73415 0.021001 0.002793 1 0.95796 0.019021 0.002291 1 0.96187 -0.313296 0.621646 1 0.4304

Joint 7.354444 7 0.3929


1 2.175084 1.077438 1 0.29932 3.764706 0.925895 1 0.33593 1.875085 2.003603 1 0.15694 1.897129 1.925848 1 0.16525 1.680886 2.755096 1 0.09696 1.898363 1.921541 1 0.16577 1.413993 3.982746 1 0.0460

Joint 14.59217 7 0.0416


1 1.191113 2 0.55132 7.254031 2 0.02663 2.174149 2 0.33724 2.041205 2 0.36045 2.757890 2 0.25186 1.923832 2 0.38227 4.604392 2 0.1000

Joint 21.94661 14 0.0797

3


Vector Autoregression Estimates

INDDUR INDNONDUR RESCOSTINDEX INVRATE DLDEFL DLCMDTY ECBRATE

INDDUR(-1) 0.360167 0.296674 0.144782 0.506300 -0.020804 1.846332 11.98971 (0.24759) (0.12959) (0.09176) (0.16298) (0.02785) (0.72384) (4.84790)[ 1.45467] [ 2.28937] [ 1.57782] [ 3.10642] [-0.74706] [ 2.55075] [ 2.47318]

INDNONDUR(-1) -0.451639 -0.463137 -0.021267 -0.258995 0.014143 -1.131532 -5.852376 (0.35052) (0.18346) (0.12991) (0.23074) (0.03943) (1.02473) (6.86315)[-1.28849] [-2.52450] [-0.16371] [-1.12247] [ 0.35874] [-1.10422] [-0.85272]

RESCOSTINDEX(-1) -1.103059 -0.250741 0.085449 -0.186957 -0.058799 -5.124501 -2.360844 (0.59545) (0.31165) (0.22068) (0.39197) (0.06697) (1.74079) (11.6589)[-1.85248] [-0.80456] [ 0.38721] [-0.47697] [-0.87793] [-2.94378] [-0.20249]

INVRATE(-1) 0.289567 0.375142 0.029374 -0.037507 -0.030901 1.177878 5.548696 (0.40384) (0.21137) (0.14967) (0.26584) (0.04542) (1.18063) (7.90725)[ 0.71703] [ 1.77485] [ 0.19626] [-0.14109] [-0.68030] [ 0.99767] [ 0.70172]

DLDEFL(-1) -0.801174 0.474800 0.061423 -0.121869 0.759919 4.039900 -15.86115 (0.88219) (0.46172) (0.32695) (0.58072) (0.09923) (2.57906) (17.2732)[-0.90817] [ 1.02832] [ 0.18787] [-0.20986] [ 7.65853] [ 1.56643] [-0.91825]

DLCMDTY(-1) 0.004822 -0.014339 0.024371 0.012299 0.001335 0.271675 0.884409 (0.06826) (0.03572) (0.02530) (0.04493) (0.00768) (0.19954) (1.33644)[ 0.07065] [-0.40137] [ 0.96343] [ 0.27373] [ 0.17388] [ 1.36148] [ 0.66176]

ECBRATE(-1) -0.005745 -0.000685 -0.000331 -0.004334 0.000947 -0.012775 0.962698 (0.00327) (0.00171) (0.00121) (0.00215) (0.00037) (0.00955) (0.06399)[-1.75780] [-0.40064] [-0.27329] [-2.01467] [ 2.57496] [-1.33703] [ 15.0439]

C 0.040418 -0.002765 0.006832 0.018732 0.002741 0.013825 0.459374 (0.02169) (0.01135) (0.00804) (0.01428) (0.00244) (0.06342) (0.42477)[ 1.86309] [-0.24351] [ 0.84970] [ 1.31171] [ 1.12330] [ 0.21798] [ 1.08146]

R-squared 0.400036 0.295479 0.219521 0.478825 0.852711 0.496602 0.889595 Adj. R-squared 0.260044 0.131091 0.037409 0.357218 0.818343 0.379142 0.863834 Sum sq. resids 0.009168 0.002512 0.001259 0.003973 0.000116 0.078361 3.514983 S.E. equation 0.017482 0.009150 0.006479 0.011508 0.001966 0.051108 0.342295 F-statistic 2.857572 1.797446 1.205416 3.937466 24.81158 4.227855 34.53241 Log likelihood 104.3422 128.9447 142.0611 120.2313 187.3726 63.57667 -8.689183 Akaike AIC -5.070641 -6.365508 -7.055850 -5.906908 -9.440662 -2.925088 0.878378 Schwarz SC -4.725886 -6.020753 -6.711095 -5.562153 -9.095907 -2.580333 1.223133 Mean dependent -0.003767 0.001700 0.007976 -4.96E-05 0.020500 0.013744 3.111842 S.D. dependent 0.020323 0.009816 0.006604 0.014354 0.004613 0.064862 0.927612


4


Appendix 4 ---- Descriptive statistics for VAR variables

DLGDP DLDEFL DLCMDTY ECBRATE Mean 0.003731 0.019833 0.009823 3.005952 Median 0.004942 0.020000 0.025422 3.000000 Maximum 0.012181 0.029000 0.103198 4.750000 Minimum -0.024832 0.008000 -0.246605 1.000000 Std. Dev. 0.006941 0.004973 0.065525 0.972939 Skewness -2.361675 -0.645217 -1.666649 0.117535 Kurtosis 9.956894 2.864052 6.873242 1.983195

Jarque-Bera 123.7397 2.946476 45.69754 1.906013 Probability 0.000000 0.229182 0.000000 0.385580

Sum 0.156710 0.833000 0.412560 126.2500 Sum Sq. Dev. 0.001975 0.001014 0.176033 38.81101

Observations 42 42 42 42

DEMGR INVGR Mean 0.003283 -0.000340 Median 0.004000 -0.000550 Maximum 0.012200 0.006800 Minimum -0.019600 -0.006600 Std. Dev. 0.005756 0.003392 Skewness -1.670412 0.133498 Kurtosis 7.349609 2.439429

Jarque-Bera 52.64036 0.674672 Probability 0.000000 0.713669

Sum 0.137900 -0.014300 Sum Sq. Dev. 0.001358 0.000472

Observations 42 42

INDDUR INDNONDUR INVRATE RESCOSTINDEX RESPER Mean -0.003491 0.001595 0.000440 0.007981 -0.001317 Median -0.003323 0.000000 0.003211 0.006965 -0.003076 Maximum 0.035742 0.026573 0.020122 0.023693 0.188244 Minimum -0.070709 -0.029163 -0.068836 -0.013203 -0.194307 Std. Dev. 0.020128 0.009708 0.014490 0.006516 0.084773 Skewness -0.830756 -0.285944 -2.759264 -0.148432 0.141131 Kurtosis 5.084510 5.387830 14.48038 5.037774 2.758915

5


Jarque-Bera 11.54693 9.796781 263.6614 6.891059 0.223914 Probability 0.003109 0.007459 0.000000 0.031888 0.894083

Sum -0.136134 0.062193 0.017179 0.311242 -0.051349 Sum Sq. Dev. 0.015395 0.003581 0.007979 0.001614 0.273086

Observations 39 39 39 39 39

Appendix 5 ---- Autocorrelations of the error terms in the VAR models

-.4

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Cor(DLGDP,DLGDP(-i))

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Autocorrelations w ith 2 Std.Err. Bounds

6


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1 2

Cor(INVGR,DLCMDTY(-i))

-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

1 2

Cor(INVGR,ECBRATE(-i))

-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

1 2

Cor(DLDEFL,DEMGR(-i))

-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

1 2

Cor(DLDEFL,INVGR(-i))

-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

1 2


-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

1 2


-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

1 2


-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

1 2

Cor(DLCMDTY,DEMGR(-i))

-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

1 2

Cor(DLCMDTY,INVGR(-i))

-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

1 2


-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

1 2


-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

1 2


-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

1 2

Cor(ECBRATE,DEMGR(-i))

-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

1 2

Cor(ECBRATE,INVGR(-i))

-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

1 2


-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

1 2


-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

1 2


Autocorrelations with 2 Std.Err. Bounds

7


-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INDDUR,INDDUR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INDDUR,INDNONDUR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INDDUR,RESCOSTINDEX(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INDDUR,INVRATE(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INDDUR,DLDEFL(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INDDUR,DLCMDTY(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INDDUR,ECBRATE(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INDNONDUR,INDDUR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INDNONDUR,INDNONDUR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INDNONDUR,RESCOSTINDEX(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INDNONDUR,INVRATE(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INDNONDUR,DLDEFL(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INDNONDUR,DLCMDTY(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INDNONDUR,ECBRATE(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(RESCOSTINDEX,INDDUR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(RESCOSTINDEX,INDNONDUR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(RESCOSTINDEX,RESCOSTINDEX(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(RESCOSTINDEX,INVRATE(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(RESCOSTINDEX,DLDEFL(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(RESCOSTINDEX,DLCMDTY(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(RESCOSTINDEX,ECBRATE(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INVRATE,INDDUR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INVRATE,INDNONDUR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INVRATE,RESCOSTINDEX(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INVRATE,INVRATE(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INVRATE,DLDEFL(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INVRATE,DLCMDTY(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(INVRATE,ECBRATE(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(DLDEFL,INDDUR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(DLDEFL,INDNONDUR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(DLDEFL,RESCOSTINDEX(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(DLDEFL,INVRATE(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2


-.6

-.4

-.2

.0

.2

.4

.6

1 2


-.6

-.4

-.2

.0

.2

.4

.6

1 2


-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(DLCMDTY,INDDUR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(DLCMDTY,INDNONDUR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(DLCMDTY,RESCOSTINDEX(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(DLCMDTY,INVRATE(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2


-.6

-.4

-.2

.0

.2

.4

.6

1 2


-.6

-.4

-.2

.0

.2

.4

.6

1 2


-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(ECBRATE,INDDUR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(ECBRATE,INDNONDUR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(ECBRATE,RESCOSTINDEX(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2

Cor(ECBRATE,INVRATE(-i))

-.6

-.4

-.2

.0

.2

.4

.6

1 2


-.6

-.4

-.2

.0

.2

.4

.6

1 2


-.6

-.4

-.2

.0

.2

.4

.6

1 2


Autocorrelations with 2 Std.Err. Bounds

8


Appendix 6 --- The Credit Channel of the Monetary Policy Transmission --- VAR models

Portugal

VAR Model - Substituted Coefficients:===============================MANINT = 0.1722600216*MANINT(-1) + 0.6307870889*ECBRATE(-1) + 0.04214250174ECBRATE = - 0.0140693548*MANINT(-1) + 0.5998469457*ECBRATE(-1) + 0.01288149014

===============================MANEMPL = - 0.4822350147*MANEMPL(-1) + 0.8135964751*ECBRATE(-1) - 0.01265526182ECBRATE = 0.04038872227*MANEMPL(-1) + 0.3553159624*ECBRATE(-1) + 0.01970002014

===============================MANPROF = - 0.1577299671*MANPROF(-1) - 0.4624307925*MANPROF(-2) - 12.41005507*ECBRATE(-1) + 8.397822584*ECBRATE(-2) + 0.1847679068ECBRATE = - 0.03564966046*MANPROF(-1) - 0.006601546333*MANPROF(-2) + 0.155443144*ECBRATE(-1) - 0.6258879383*ECBRATE(-2) + 0.0419648996

===============================WREMPL = 1.051685581*WREMPL(-1) - 0.1572162884*ECBRATE(-1) + 0.004025801068ECBRATE = 0.3623164219*WREMPL(-1) + 0.4597463284*ECBRATE(-1) - 0.005434245661

===============================WRINT = - 0.2990440279*WRINT(-1) - 0.02595295777*WRINT(-2) + 13.09548493*ECBRATE(-1) - 9.354749615*ECBRATE(-2) - 0.06042302231ECBRATE = 0.02111475131*WRINT(-1) + 0.008995143777*WRINT(-2) + 0.5874104313*ECBRATE(-1) - 0.6188519084*ECBRATE(-2) + 0.02877219451

===============================WRPROF = - 0.8289380816*WRPROF(-1) - 0.7303142038*WRPROF(-2) + 8.18286834*ECBRATE(-1) + 11.10313038*ECBRATE(-2) - 0.4900944915ECBRATE = - 0.01431503405*WRPROF(-1) - 0.01416893864*WRPROF(-2) + 0.4209865662*ECBRATE(-1) - 0.1598797706*ECBRATE(-2) + 0.02096702624

Germany

VAR Model - Substituted Coefficients:===============================MANEMPL = - 0.5597115709*MANEMPL(-1) - 3.952251627*ECBRATE(-1) + 0.1489661717ECBRATE = 0.05877209065*MANEMPL(-1) + 0.373037899*ECBRATE(-1) + 0.01814748149

===============================MANINT = - 1.347875151*MANINT(-1) + 26.07863407*ECBRATE(-1) - 0.7252988106ECBRATE = - 0.04295307764*MANINT(-1) + 0.9182840531*ECBRATE(-1) + 0.002856707772

===============================

9


MANPROF = - 0.593793869*MANPROF(-1) + 2.217600661*ECBRATE(-1) - 0.1451789807ECBRATE = 0.01882326539*MANPROF(-1) + 0.279426167*ECBRATE(-1) + 0.02127466349

===============================WREMPL = 0.1654155399*WREMPL(-1) - 1.277022113*ECBRATE(-1) + 0.06420173844ECBRATE = 0.03275973779*WREMPL(-1) + 0.2996853803*ECBRATE(-1) + 0.02139295535

===============================WRINT = 0.9282991305*WRINT(-1) - 7.894198452*ECBRATE(-1) + 0.2212409629ECBRATE = 0.1045924685*WRINT(-1) - 0.8609232001*ECBRATE(-1) + 0.05891676573

Italy

VAR Model - Substituted Coefficients:===============================MANEMPL = 0.3763054307*MANEMPL(-1) + 0.8129629261*ECBRATE(-1) - 0.03858196417ECBRATE = - 0.1168075254*MANEMPL(-1) + 0.1132424706*ECBRATE(-1) + 0.02512915141

===============================MANINT = 0.1638296664*MANINT(-1) + 0.3482310064*MANINT(-2) + 4.102219608*ECBRATE(-1) - 9.363328734*ECBRATE(-2) + 0.143216333ECBRATE = 0.08411452008*MANINT(-1) + 0.03675315532*MANINT(-2) - 0.06395391847*ECBRATE(-1) - 0.5709230309*ECBRATE(-2) + 0.05031012254

===============================MANPROF = - 0.0573312392*MANPROF(-1) - 23.19878919*ECBRATE(-1) + 0.7154212588ECBRATE = - 0.001647685651*MANPROF(-1) - 0.1259742922*ECBRATE(-1) + 0.04071142968

===============================WREMPL = - 1.231411856*WREMPL(-1) - 0.809334451*WREMPL(-2) - 7.043423486*ECBRATE(-1) - 0.9882278419*ECBRATE(-2) + 0.2373336544ECBRATE = - 0.08802173422*WREMPL(-1) + 0.01662110708*WREMPL(-2) + 0.3997186738*ECBRATE(-1) - 0.5477291069*ECBRATE(-2) + 0.03222402079

===============================WRINT = 1.233993558*WRINT(-1) + 0.4075975426*WRINT(-2) - 3.423094135*ECBRATE(-1) - 9.40937419*ECBRATE(-2) + 0.4143224998ECBRATE = 0.1145733652*WRINT(-1) - 0.002262337339*WRINT(-2) - 0.48412269*ECBRATE(-1) - 0.03616606616*ECBRATE(-2) + 0.04454624468

===============================WRPROF = - 1.145935521*WRPROF(-1) - 0.4331807453*WRPROF(-2) + 59.93006368*ECBRATE(-1) + 68.61521639*ECBRATE(-2) - 3.333182541ECBRATE = 0.0177282427*WRPROF(-1) + 0.003509581896*WRPROF(-2) + 0.7942810404*ECBRATE(-1) - 1.813069335*ECBRATE(-2) + 0.05393020072

10


Appendix 7 ---- VAR models in theory

(The textbook used here is “Applied econometrics, A modern approach”, Dimitrios Asteriou and Sthephen G. Hall, revised edition 2007, Palgrave Macmillan

Method - Shortly about VAR

The vector auto-regression technique, or VAR, uses a system of ordinary least-squares regressions, in which each of a set of variables is regressed on lagged values of both itself and the other variables in the set.

In many models of the economic theory have variables that are both explanatory and also explained by the variables that they are used to determine, therefore it is difficult to determine which are exogenous and which are endogenous. For example, when a Central Bank makes a key interest rate decision, it takes into consideration the economical situation at that moment. On the other hand, the ‘new’ key interest rate will further have an effect in the economy. Therefore, at a time series level, we cannot be confident that the key interest rate is really exogenous.

In a VAR model, each equation has the same set of regressors, meaning that all the variables are treated as endogenous122. For example, have the time series yt, affected by the present and past value of xt and, at the same time, xt is affected by the present and past values of yt. In this case we will have a simple bivariate model, as follows:

yt= β10- β12xt+γ11yt-1+ γ12xt-1+uyt

xt= β20- β21yt+γ21yt-1+ γ22xt-1+uxt

with the assumptions that yt and xt are stationary time series and uyt, uxt are uncorrelated white-noise error terms. The VAR model above is a non-reduced form, first-order model, as y t and Xt have a contemporaneous impact on each other (- β12 , - β21 )and the longest lag-length is unity.

In a matrix form, the system looks as follows:

[ 1 β12

β21 1 ][ y t

xt ]=[β10

β20 ]+[γ 11 γ12

γ21 γ22][ yt−1

xt−1]+[uyt

uxt ]equivalent of: Βz t = Γ0 + Γ1zt-1 + ut , (4)where:

Β =[ 1 β12

β21 1 ], zt= [ y t

x t ], Γ0 = [ β10

β20], 122 Sims (1980) argues that when there is simultaneity among a number of variables , then all of them should be treated in the same way.

11


Γ1 = [γ 11 γ 12

γ 21 γ 22] and ut = [uyt

uxt ]Further, if we multiply to the left by Β-1:

/*Β-1 Βzt = Γ0 + Γ1zt-1 + ut

zt = A0 + A1zt-1 + et

where A0=B-1 Γ0, A1= B-1 Γ1 and et= B-1 ut

For notational simplification, we can denote as a i0 the ith element of vector A0, aij the element of A1 situated on line i and column j, and e it the ith element of the vector et, so we can rewrite the VAR model as:

yt= a10 +a11yt-1+ a12xt-1+e1t

xt= a20 +a21yt-1+ a22xt-1+e2t

This second model is a VAR in standard (or reduced) form. The new error terms, e1t and e2t, are composites of the two shocks uxt and uyt. As et= B-1 ut, e1t and e2t can be re-written as:

e1t = (uyt+β12uxt) / (1-β12β21)e2t = (uxt+β21uyt) / (1-β12β21)

Since uxt and uyt are white-noise process, e1t and e2t are also white-noise processes, as they are derived from the former through a scalar transformation.

(Dis)Advantages of the VAR models

There are several reasons of which the VAR approach is preferred in the economic research. First of all, the model is very simple and we don’t have to worry about which variables are exogenous or endogenous. Moreover, the estimation is very simple, as each equation can be estimated using the usual OLS method. Previous research has even proven that the forecasts obtained through a VAR model are better than those obtained using more complex equation models (Mahmoud, 1984; McNees, 1986).

On the other hand, the VAR method is considered to be a-theoretic, since they are not based on any economic theory. As they have no restrictions concerning the parameters to be estimated, in effect “everything causes everything”123. Still, if some coefficients appear to be insignificant in the estimated models, they can be dropped, leading to models that might have an underlying consistent theory. Such statistical inference can be carried out using the so called causality tests.

Another disadvantage of the VAR method aims the loss of degrees of freedom. For example, in a three variable VAR model with 12 lags for each variable in each equation would lead to an estimation of 36 parameters in each equation plus the constant. If the sample is not large enough, estimating the parameters will consume many degrees of freedom, creating problems in the estimation.

Moreover, the coefficients of a VAR model are actually difficult to interpret due to the lack of any theoretical background. However, the solution to this is to estimate the so called impulse response

123 Dimitrios Asteriou and Stephen G. Hall, “Applied Econometric - A modern approach”, 2007

12


functions that examine the response of the endogenous variables in the VAR model to shocks in the error terms. The general method is to shock the structural error, i.e. u xt and uyt from the non-reduced form VAR, but we can only observe the reduced form errors which are a combination of the structural errors. The methods used to disentangle the structural errors are different and they can lead to different results. Still, as there is no objective statistical criteria to choose one of these methods, it remains a matter of professional judgment.

Causality tests

1. The Granger causality test:

For the case of two stationary variables, xt and yt, the first step implies the estimation of the following VAR model:

yt = a1 + ∑i=1

n

β i xt−1+∑j=1

n

γ y y t− j+e1t

xt = a2 + ∑i=1

n

θ i x t−1+∑j=1

n

δ y y t− j+e2t

ext, eyt being uncorrelated white-noise error terms.

Then, if:

(1) the lagged x terms may be statistically different from 0 as a group, y terms not statistically different => xt causes yt

(2) the lagged y terms may be statistically different from 0 as a group, x terms not statistically different => yt causes xt

(3) both sets of x and y terms are statistically different from 0 => bi-directional causality(4) both sets of x and y terms are not statistically different from 0 => xt and yt are independent

The Granger causality test involves the following procedure (similar for xt):

Step1 Regress yt on lagged y terms: yt=a1 + ∑j=1

n

γ j y t− j+ e1t

and obtain the RSS of this regression (which is the restricted regression) = RSSR

Step2 Regress yt on lagged x terms and lagged y terms:

yt=a1 +∑i=1

n

β i xt−i ∑j=1

n

γ j y t− j+ e1t

and obtain the RSS of the regression (the unrestricted regression) = RSSU

Step3 Set the null and alternative hypotheses:

H0: ∑i=1

n

β i=0 or xt does not cause yt

13


H1: ∑i=1

n

β i≠0 or xt causes yt

Step4 Run the F statistics for the normal Wald test on coefficient restrictions given by

F=(RSS¿¿ R−RSSU) /m

RSSU / (n−k )¿

Which follows a Fm, n-k distribution, k=m+n+1

Step5 If computed F>F-critical, reject the null hypothesis => xt causes yt

2. The Sims (1980) causality test:

Sims’ alternative test assumes that in any general notion of causality, it is not possible for the future to cause the present. Therefore, to check whether y t causes xt, we estimate the following VAR model:

yt = a1 + ∑i=1

n

β i xt−1+∑j=1

n

γ y y t− j+∑p=1

k

ϑ p x t+ p+e1 t

xt = a2 + ∑i=1

n

θ i x t−1+∑j=1

n

δ y y t− j+∑p=1

k

ϑ p y t+p+e2t

including also leading values of x (respectively y) in the equations.

Again, taking the first equation, if yt causes xt, then we expect to find some relationship

between y and the leading values of x, therefore we test for ∑p=1

k

ϑ p=0. If we reject the null hypothesis,

then the causality runs from yt to xt and not vice versa, as the future cannot cause the present.

The steps are similar to the Granger test:

1. We estimate xt = a2 + ∑i=1

n

θ i x t−1+∑j=1

n

δ y y t− j+e2t

(the restricted version, no leading terms => RSSR)

2. We estimate xt = a2 + ∑i=1

n

θ i x t−1+∑j=1

n

δ y y t− j+∑p=1

k

ϑ p y t+p+e2t

(the unrestricted version, including leading terms => RSSU)3. Set the null hypothesis4. Run the F test as above5. Draw the conclusion

It is not very clear which of the two versions are preferable and most of the researchers use both. However, it must be mentioned that the Sims test uses more regressors, leading to a bigger loss of degrees of freedom.

14

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