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Bank of Zambia
Working Paper
WP/01/2014
Monetary Policy Transmission
Mechanism in Zambia**
Patrick Chileshe, Francis Z. Mbao, Brenda Mwanza, Ladslous Mwansa, Tobias
Rasmussen, and Peter Zgambo
Abstract
This study seeks to determine the relevance and magnitude of the different channels of the
monetary transmission mechanism in Zambia. Autoregressive methods are used to
empirically identify the impact of monetary policy on macroeconomic outcomes. The results
indicate that the direct link from interest rates to output and inflation in Zambia has
historically been weak and that monetary aggregates have, at least until recently, had a
greater role in the monetary transmission mechanism.
Disclaimer: The views expressed in this paper are those of the authors and do not reflect the
official position of the Bank of Zambia.
** This paper done in collaboration with the International Monetary Fund (IMF) Resident
Office
2
Table of Contents I. Introduction ........................................................................................................................... 3
II. Review of Theoretical Literature on the Monetary Transmission Mechanism .................... 4
A. Interest Rate Channel ....................................................................................................... 5
B. Exchange Rate Channel ................................................................................................... 5
C. Credit Channel.................................................................................................................. 6
D. Asset Price Channel ......................................................................................................... 7
E. Expectations Channel ....................................................................................................... 7
III. Review of Empirical Literature on the Monetary Transmission Mechanism ..................... 8
IV. Evolution of Monetary Policy and Economic Variables since 1964 .................................. 9
V. Model Estimations ............................................................................................................. 12
A. Links Between Interbank and Lending Rates ................................................................ 13
B. Broader Linkages between Variables in Monetary Policy Transmission ...................... 14
Granger Causality Tests .................................................................................................. 14
VAR Model Estimation .................................................................................................. 16
VI. Conclusions....................................................................................................................... 22
REFERENCES ....................................................................................................................... 24
APPENDIX 1: ......................................................................................................................... 29
APPENDIX 2 .......................................................................................................................... 31
Table A1: Lending Rates and Interbank Rate Estimations, Entire Sample Period ......... 31
Table A2: Lending Rates and Interbank Rate Estimations, High Volatility Era ............ 31
Table A3: Lending Rates and Interbank Rate Estimations, Low Volatility Era ............. 32
APPENDIX 3 .......................................................................................................................... 33
Table A4: Pairwise Granger Causality with Policy Rate, 2012M4-2013M9 ................. 33
Table A5: Granger Causality Tests, 1995M1-2013M9 .................................................. 33
APPENDIX 4 .......................................................................................................................... 36
Figure A1: Model 2-Impulse Response .......................................................................... 36
Table A6: Model 2-Variance Decomposition ................................................................. 37
Figure A2: Model 3-Impulse Responses......................................................................... 38
Table A7: Model 3-Variance Decomposition ................................................................. 39
Figure A3: Model 4-Impulse Responses......................................................................... 40
Table A8: Model 4-Variance Decomposition ................................................................. 41
Figure A4: Model 5-Impulse Responses......................................................................... 42
Table A9: Model 5-Variance Decomposition ................................................................. 43
Figure A5: Model 6-Impulse Responses......................................................................... 44
Table A10: Model 6-Variance Decomposition ............................................................... 45
Figure A6: Model 1 (Early Sub-period)-Impulse Responses ......................................... 46
Table A11: Model 1 (Early Sub-period)-Variance Decomposition ................................ 47
Figure A7: Model 1 (Late Sub-period)-Impulse Responses ........................................... 48
Table A12: Model 1 (Late Sub-period)-Variance Decomposition ................................. 49
3
I. INTRODUCTION
The aim of this study is to strengthen the understanding of how monetary policy, and in
particular interest rate setting, affects the Zambian economy. Effective monetary policy
depends on a central bank having a firm understanding of the link between its actions and its
objectives. The main objective of the Bank of Zambia (BoZ) is to ensure price and financial
system stability in Zambia. This objective has traditionally been pursued by targeting
monetary aggregates. In light of changes in the economy and to better anchor inflation
expectations, BoZ is now in the process of shifting its monetary policy framework to
targeting interest rates. A key step in this direction was the introduction of the BoZ Policy
Rate in April 2012. Uncovering how the Zambian economy responds to changes in interest
rates has accordingly gained importance.
The effect of interest rate setting on the broader economy works through what is termed the
monetary transmission mechanism (MTM): the process through which monetary policy
decisions are transmitted into economic activity and prices. This process is one that links
central banks’ operational targets (typically short term interest rates or reserve money) to its
intermediate targets (medium and long-term interest rates, broad money, credit, and exchange
rate) and eventually to its goal targets (inflation and output). The literature on the monetary
transmission mechanism identifies five main channels, which are discussed in detail in
Section III.
The aim of this study is to determine the relevance and magnitude of the different channels of
the MTM in Zambia. To do so, the analysis takes an empirical approach, building on earlier
work of, among others, Simatele (2004), Mutoti (2006), and Baldini et al. (2012. These
studies all support the presence of the credit and exchange rate channels in Zambia, with the
latter also identifying the presence of the interest rate channel. In addition to including more
recent data, this study builds on the previous litterature by evaluating differences in the
effectiveness of interest rates and monetary aggregates in the MTM, and it also seeks to
evaluate how these relationships may have changed over time.
While the ultimate aim of this research is to understand how a change in the BoZ policy rate
can be expected to influence the economy, a key challenge is that the policy rate has not been
in existence for very long. Up to now, the policy rate has only been changed a few times and
the scope for uncovering patterns in the way the economy has responded is correspondingly
limited. The analysis therefore looks at two other short-term interest rates, the interbank rate
and the 90 days T-bill yield rate, which can be seen as proxies for the policy rate. It then
examines how changes in these two interest rates have impacted on the central bank’s
intermediate and ultimate targets.
The main finding is that the direct link from interest rates to output and inflation in Zambia
has been weak and that monetary aggregates have, at least until recently, had a greater role in
4
the MTM. While the exchange rate channel is clearly present and there is also some evidence
to suggest that interest rates are gaining in importance compared to monetary aggregates, it is
therefore still too early to abandon the traditional policy focus on monetary aggregates.
Instead, an important objective should be to enhance the MTM to enhance to effectiveness of
monetary policy whether delivered via monetary aggregates or through the BoZ policy rate.
The rest of this paper is organized as follows. Sections II and III provide a review of the
theoretical and empirical literature. Section IV outlines the evolution of the Zambian
economy since the early 1960s. Section V presents a series of models and estimations.
Section VI concludes.
II. REVIEW OF THEORETICAL LITERATURE ON THE MONETARY TRANSMISSION
MECHANISM
The MTM determines how policy-induced changes in the nominal money stock or short-term
interest rates impact on output and inflation (Ireland, 2005). The neo-classical view of long-
run neutrality of money is widely accepted. Nevertheless, monetary policy is in the short run
thought to influence economic activity through changes in interest rates or money supply,
either because of nominal price rigidities (Keynesian view) or owing to a number of wealth,
income, liquidity, and expectation effects (Dabla-Norris and Floerkemeier, 2006). Although
different classifications have been made and there is some overlap, the theoretical literature
identifies five main channels of the MTM. These are the interest rate, exchange rate, bank
lending, asset price, and expectations channels (Bank of England, 1999; Horvath et al., 2006;
Loayza et al., 2002; Mishkin, 1995; Obstfeld et al.,1995; Taylor, 1995). The transmission
channels are graphically illustrated in Figure 1 and described below.
Figure 1: Monetary Policy Transmission Channels
Source: Adapted from Loayza et al., (2002) and Bank of England (1999).
Source: Adapted from Loayza et al.(2002) and Bank of England (1999).
Monetary
policy
instrument
Interest rates
Credit
Asset prices
Exchange
rates
Domestic
demand
External
demand
Total
demand
Domestic
inflationary
pressure
Import
prices
Inflation
Expectations
5
A. Interest Rate Channel
The interest rate channel is considered the primary MTM in traditional models that operate
by altering the marginal cost of lending and borrowing and thereby produce changes in
investment, saving, and aggregate demand (Horvath at al., 2006). Key to the interest rate
channel is the notion that if prices are sticky then central bank actions that change nominal
short term interest rates also change real interest rates and ultimately output. In this setting,
an expansionary monetary policy leads to a fall in real interest rates, which in turn stimulates
investment due to a decline in the cost of borrowing. An increase in investment leads to an
increase in aggregate demand and output, which may in turn result in increased inflationary
pressures in the economy.
The basic functioning of the interest rate channel has remained relatively unchanged as the
literature on monetary transmission has evolved. The mechanism is still present in theories
that incorporate expectations (Butkiewicz and Ozgdogan, 2009; Clarida, Gali and Gertler,
1999; Rotemberg and Woodford, 1998). Moreover, other studies have extended the
Keynesian focus on business investment to include effects on spending on housing and other
consumer durables as well as substitution effects in consumption spending (Butkiewicz and
Ozgdogan, 2009; Els, Locarno, Morgan and Viletelle 2003; Taylor, 1995).
While changes in the central bank’s policy rate are expected to be immediately transmitted to
short-term money market rates, several factors influence the effectiveness of the interest rate
channel. These include the structure and competiveness of banking sector, the size of the
shadow informal sector, and the speed with which the policy rate is transmitted to
commercial lending rates (Bank of England, 1999; Tahir, 2012; Horvath et al., 2006; Dabla-
Norris, 2012).
B. Exchange Rate Channel
The exchange rate channel works through the effect that monetary policy, via the exchange
rate, has on imports and exports (Horvath et al., 2006). Monetary policy can influence the
exchange rate through interest rates via the uncovered interest rate parity (UIP) condition,
through direct intervention in foreign exchange markets, or through inflationary expectations
(Dabla-Norris et al., 2006).
The link between monetary policy and exchange rate under the UIP condition was
popularized by the open macroeconomic models developed by Fleming (1962), Mundell
(1963), and Dornbusch (1976). Under the UIP, the difference between the domestic and
foreign interest rate is equal to the expected exchange rate change. Accordingly, monetary
policy induced changes in domestic interest rates therefore change exchange rate
expectations and hence the relative price of imports and exports, which in turn affects
aggregate demand and supply. On the demand side, monetary policy that lowers domestic
interest rates will cause the currency to depreciate, making exports cheaper and imports more
6
expensive increasing net exports and hence impacting positively on aggregate demand and
output (Obstfeld and Rogoff, 1995; Taylor, 1993; Mishkin, 1996, 2001). On the supply side,
however, the higher domestic price of imported goods increases inflationary pressure and
contracts output (Chipili, 2013; Ozdogan, 2009; Kara, Tuger, Ozlale, Tuger, Yavuz and
Yucel, 2005; Campa and Goldberg, 2004; Alper 2003; Loazya and Schmidt-Hebbel, 2002;
McCallum and Nelson, 2001).
The effectiveness of the exchange rate channel in the transmission mechanism depends on
the extent of the pass-through to domestic prices, which in turn depends on the import share
and other characteristics of the economy. In general, the larger the import share or the more
the economy is dollarized, the larger the exchange rate pass-through (Horvath et al., 2006).
C. Credit Channel
Benanke and Gertler (1995) propose the bank lending (credit) channel, which explains the
MTM as an outcome of credit market imperfections arising from asymmetric information and
costly enforcement of contracts in financial markets. The basic notion underlying this
channel is that monetary policy can have price and output effects through credit rationing that
arises from information asymmetries between financial institutions and the firms and
consumers to which they lend (Loayza et al., 2002). This occurs because monetary policy
affects the extent of adverse selection and moral hazard that constrain the provision of credit
in the economy. It is argued that monetary expansion reduces adverse selection and moral
hazard problems by increasing firm’s net worth, reducing perceived loan risks, improving
firms’ cash flow, and decreasing the burden of nominal debt contracts (Loayza et al., 2002).
The credit channel has two sub-channels—the bank lending and the balance sheet channels.
The bank lending sub-channel works by influencing banks’ ability to make loans following
changes in the monetary base (Kishan and Opiela, 2000; Kashyap and Stein, 2000; Huang,
2003; Sichei, 2005). Here, a policy induced expansion of the monetary base increases the
amount of reserves (deposits) available to banks, which they can use to advance loans. An
expanded monetary base is thus likely to increase lending for investment and consumption
purposes, leading to a rise in investment and consumption spending. The increase in
domestic demand raises aggregate demand and, if aggregate demand exceeds aggregate
supply, also inflationary pressures in the economy.
The balance sheet sub-channel is premised on the prediction that the external finance
premium that a borrower faces depends on the borrower’s net worth. In this regard, monetary
policy can have direct and indirect effects on borrowers’ balance sheets. A direct effect arises
when an increase in interest rates works to raise the payments a borrower must make to
service debts, while an indirect effect arises when interest rates reduce the capitalized value
of the borrower’s assets (Ireland, 2006). As a result, an increase in interest rates arising from
tight monetary policy depresses spending through the traditional interest rate channel, but
7
also raises the borrowers’ cost of capital through the balance sheet channel. This reduces
investment, consumption, employment, and output, and puts downward pressure on prices.
Factors that strengthen the credit channel include the magnitude of bank capitalization, the
degree of development of the securities markets, and the size of firms in the economy
(Putkuri, 2003, and Tahir, 2012).
D. Asset Price Channel
The asset price channel is premised on the idea that monetary policy can have important
effects on prices of assets such as bonds, equity, and real estate (Bank of England, 1999;
Loayza et al., 2006). As noted by Horvath et al. (2006), the asset price channel operates
through changes in the market value of firms and the wealth of households. Here, changes in
monetary policy alter the relative price of new equipment, thereby affecting firms’
investment spending and their market value. Changes in monetary policy also affect
households’ collateral for borrowing, thereby affecting consumption spending.
According to the theory of the asset price channel, expansionary monetary policy results in
higher equity prices because the expected future returns are discounted by a lower factor,
thereby raising the present value of any given future income stream. Higher equity prices
makes investment more attractive (e.g., through Tobin’s q), which raises aggregate demand.
Higher equity prices also entail increased household wealth, which raises consumption and
aggregate demand (Loayza et al., 2006). In turn, the changes in aggregate demand impact on
output and inflation.
Tahir (2012) highlights the following factors as key to the strength of the asset price channel:
the degree of household participation in the capital market; the prevalence of firms that raise
funds through issuance of shares; and the level of development of the national stock market.
This is confirmed by Kamin et al. (1998) and by Butkiewicz and Ozgdogan (2009) who note
that the asset price channel in developing and emerging markets is weak and more
unpredictable compared to developed economies due to shallower and less competitive
markets as well as more unstable macroeconomic environments.
E. Expectations Channel
The four monetary transmission channels noted above focus on money and asset markets.
However, the literature also identifies a fifth channel, which is based on the private sector’s
expectations about the future stance of monetary policy and related variables (Loayza et al.,
2002). This reflects the notion that monetary policy changes can influence expectations about
the future course of real activity and the confidence with which those expectations are held.
Changes in perception will then affect the behaviour of participants in financial markets and
other sectors of the economy through, for instance, changes in expected future labour
income, unemployment, sales, and profits (Bank of England, 1999).
8
Although the importance of expectations is well established, the direction in which the effect
will work is not easy to predict. For example, the Bank of England (1999) notes that an
increase in the policy rate may lead economic agents to think that the monetary authorities
believe that the economy is likely to be growing faster than previously thought, giving
expectation of future growth and confidence in general. There is, however, also the
possibility that economic agents interpret a rate hike as signalling a perceived need by the
monetary authority to slow growth to achieve the inflation target, which would impact
negatively on growth expectations and confidence. Hence, depending on how expectations
are formed, the impact of monetary policy change could be very different.
In the following section, we review the empirical literature on the monetary transmission
mechanism, focusing on Africa and the Zambian economy in particular.
III. REVIEW OF EMPIRICAL LITERATURE ON THE MONETARY TRANSMISSION
MECHANISM
Vast empirical work has been undertaken on the MTM in both developed and developing
countries. In developed economies, all the above-listed channels from the theoretical
literature have been found to play some role in the transmission of monetary policies. In
developing economies, however, empirical studies have found rather limited roles for some
of the channels—as could be expected from these countries’ relatively small banking
systems, shallow financial markets, and weak institutional frameworks (Mishra et al. 2010;
Al-Mashat et al., 2007; Bakradze et al., 2007; Dabla-Norris et al., 2006; Egert et al., 2006;
Smal et al., 2001; Montiel, 1990).
Studies looking at Africa suggest that, in this part of the world, the interest rate channel is
generally weak, and that the credit and exchange rate channels are more important although
not always very strong. In particular, Buigut (2009) found the interest rate channel to be of
relatively little importance in the transmission of monetary policy to output and prices in the
East African region. Al-Mashat et al. (2007) found similar results for Egypt.1 Chibber and
Sharik (1990) found that the credit channel was via money creation the main transmission
channel in Ghana. Caneti and Green (1991) analysed the impact of monetary growth and
exchange rate developments in ten African countries and found that though both factors were
important in the inflationary process in most of the countries examined, neither money nor
exchange rate developments had a dominant role.
1 For South Africa, a number of empirical studies have identified the interest rate channel as the dominant
channel (Aron et al., 2000; Smal et al., 2001), likely reflecting the more the advanced nature of the financial
sector compared to other African economies.
9
Studies looking specifically at Zambia point to a relatively important role for the exchange
rate channel. Using error correction and vector autoregression (VAR) methodologies,
Mwansa (1998) found the exchange rate to have a significant and much stronger impact on
inflation than money growth. Mutoti (2006), reached a similar conclusion by employing a
cointegrated structural vector autoregression methodology. This second study further
established that the impact of money supply shocks on Zambia’s output tended to be small
and temporary, and that such shocks have had little bearing on inflation dynamics,
particularly in the long-run. A third study by Simatele (2003) used a VAR methodology and
also found the exchange rate channel to be the most important and further concluded that
bank lending in Zambia is not driven by monetary policy but rather by demand. A forth and
more recent study by Baldini (2012), which used a dynamic stochastic general equilibrium
model, confirmed the presence of the exchange rate channel but also pointed to a role for the
credit channel.
IV. EVOLUTION OF MONETARY POLICY AND ECONOMIC VARIABLES SINCE 1964
The conduct of monetary policy in Zambia can be divided into two broadly distinct periods:
The pre-liberalization period spanning from 1964 to 1991 and the post-liberalization period
from 1992 on.
During the first period, prior to 1992, monetary policy had multiple and poorly defined
objectives and its implementation relied mainly on direct instruments. The latter included
controlled interest rates and directed credit allocation, as well ratios on core liquid assets and
statutory reserve requirement. The dependence on these direct instruments originated in the
realization by the newly independent central bank that it had little control over money
supply. As noted by Kalyalya (2001), despite the central bank being empowered through the
Bank of Zambia Act of 1965 to implement monetary policy, BoZ had little grip on the
growth of credit in the economy. The banking sector was dominated by foreign banks who
issued loans to mostly foreign owned companies with little regard to domestic economic and
financial conditions. Further, during much of this period, the financing of the government
budget relied heavily on borrowing from the central bank.
Macroeconomic conditions deteriorated steadily during this period. The persistent use of
central bank financing by government as well as failure of the monetary authority to control
money supply resulted in growing inflationary pressure (Bigstern and Mugerwa, 2000).
These problems were further compounded by internal and external imbalances as well as
structural and institutional deficiencies. Domestically, price controls on most food items,
widespread consumer subsidies, and the industrialization strategy of import substitution
coupled with weak public administration worsened the fiscal position and led to a highly
inefficient allocation of resources On the external front, the country’s balance of payment
position became unsustainable following the loss of international reserves due to growing
10
foreign debt servicing and dwindling export earnings resulting from falling prices and output
of copper.
The combined effect of the factors above pushed the economy to stagnation and near
hyperinflation (Table 1 and Figures 2-3). Annual economic growth fell from an average of
3.9 percent during 1961-65 to 1.1 percent during 1981-90. At the same time, external debt as
percentage of GDP rose from 49 to 119 percent of GDP. Inflation reached an average of 76.9
percent during the 1980s, and with negative real interest rates, the banking system started to
lose its intermediation role and credit to the private sector declined relative to GDP.
The second period, staring in 1992, began as a new government came into power with an
agenda to restore economic growth through market-based stabilization policies and
promotion of the private sector. Market forces were given greater role in the allocation of
resources as prices were decontrolled and most subsidies abolished.
Table 1: Evolution of Key Monetary and Economic Variables
Source: World Bank Database, and BoZ database.
Note: The real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator.
Changes in the economic environment carried through to the conduct of monetary policy.
The amended Bank of Zambia Act No. 43 of 1996 narrowed down the central bank’s
objective to price and financial system stability. Consequently, monetary policy concentrated
on creating a stable macroeconomic environment to support sustainable economic growth.
Under the new framework, BoZ started to target monetary aggregates, an approach premised
on a strong relationship between the ultimate target (inflation) and money supply.
In its conduct of monetary policy, BoZ increasingly relied on indirect rather than direct
instruments. The indirect instruments included primary auctions of treasury bills and
government bonds, as well as auctions of short-term credit and term deposits to and from
commercial banks. In addition, the central bank’s purchases and sales of foreign exchange
were used as a tool of monetary policy. With these indirect instruments, the BoZ tried to
Indicator Name 1961-1970 1971-1980 1981-1990 1991-2000 2001-2010 2011 2012
Real Per Capita GDP Growth (annual % growth) 0.8 -1.9 -1.8 -1.7 2.8 3.6 4.0
Real GDP Growth (annual % growth) 3.9 1.5 1.1 0.8 5.6 6.8 7.3
Average Annual Inflation Rate - 11.1 76.9 68.1 15.5 6.4 6.6
External Debt Stocks (% of GNI) - 75.3 206.1 214.3 89.9 27.4 27.6
External Debt(% of GDP) - 48.7 119.3 147.3 67.9 18.1 19.0
Total Debt Service (% of exports ) 2.9 26.2 25.1 25.0 12.9 2.2 2.2
Total Reserves (% of total external debt) - 10.1 2.8 2.8 23.1 47.0 56.5
Total Reserves (% of GDP) 18.6 7.1 4.5 5.0 9.1 12.1 14.7
Broad Money (% of GDP) 19.3 29.0 30.9 18.2 21.3 23.4 24.1
Broad Money Growth (annual % growth) 27.2 10.5 41.5 49.9 22.7 21.7 17.9
Real Interest Rate (%) - 0.8 -15.5 3.1 11.3 5.6 5.6
Domestic Credit (% of GDP) -0.3 41.9 63.9 59.6 28.2 18.1 18.5
Domestic Credit to Private Sector (% of GDP) 8.5 17.1 14.0 7.5 9.6 12.3 14.8
External Balance (% of GDP) 15.1 0.9 -1.7 -6.9 -2.4 9.0 -
11
influence the behavior of financial institutions and other market players through market
mechanisms rather than relying on direct instruments, as it had before. This helped improve
control of money supply and inflation and also promoted a more efficient allocation of credit
and financial market development in general.
The change in the monetary policy framework and its implementation contributed to a
marked improvement in Zambia’s macroeconomic environment. Money growth and inflation
declined sharply, with the latter being held in the single digits since 2006. The liberalization
of lending and deposit rates initially caused real interest rates to spike but they subsequently
leveled off at about 7 percent. Moreover, since the late 1990s, real GDP growth steadily
increased, reaching an average of 7.2 percent a year during 2010-12.
Figure 2: Trends in Real Growth and Real Interest Rates since 1971
(Three-year moving averages, in percent)
Source: BoZ database and computations by authors.
The task of maintaining price stability as the economy evolved was not without challenges
for BoZ. In particular, the relationship between money growth and inflation has weakened as
the two have declined. In response, the BoZ started to move towards targeting inflation rather
than monetary aggregates. A key step in this direction was the introduction of the BoZ policy
rate in April 2012. This policy rate is reviewed each month with a view to price
developments, and BoZ has been intervening in the money market if the interbank rate
slipped out of a corridor set at +/-2 percent from the policy rate.
-40
-30
-20
-10
0
10
20
-4
-2
0
2
4
6
8
1971 1976 1981 1986 1991 1996 2001 2006 2011
Real GDP growth (LHS)
Real interest rate (RHS)
12
Figure 3: Trends in Broad Money Growth and Inflation since 1971
(in percent)
-40
0
40
80
120
160
200
1975 1980 1985 1990 1995 2000 2005 2010
Annual Broad money growth
Annual CPI Inflation
Post-1995 correlation=0.52Pre-1995 Correlation=0.70
Source: BoZ database and computations by authors.
V. MODEL ESTIMATIONS
This section presents a number of estimations carried out to uncover the monetary
transmission mechanism in Zambia. First, we examine the interest rate channel by looking at
the link between interbank and lending rates—a key component of interest rate pass-through
to the wider economy. Next, we bring in a wider set of variables to examine the full set of
possible channels, using pair-wise Granger causality tests and VAR models to identify
linkages.
The estimations are carried out on monthly and quarterly data spanning January 1995 to
October 2013. For Zambia, the data series cover interest rates (interbank, average lending
and deposit, and 90-day T-bills), money supply, exchange rates, credit, consumer prices,
industrial production, and electricity generation. In addition, the US federal funds rate,
international oil prices, and an international commodity price index were used in some
specifications. Details on the data are provided in Appendix 1. In the absence of high
frequency data on real GDP, we use the monthly series on electricity generation and the
quarterly series on industrial production as proxies for overall economic output.
13
A. Links Between Interbank and Lending Rates
Even a quick glance at the data reveals that the link between interbank and lending rates in
Zambia has historically been weak, especially in the recent past. Figure 4 shows that
interbank rates during 1995-2006 were much more volatile than lending rates, although the
two were positively correlated. After 2006, the volatility of the interbank rate diminished,
and the level of both these interest rates has also been much lower, but the correlation
between the two series has been negative.
Figure 4: Lending and Interbank Rates, Jan. 1995 – Oct. 2013
0
10
20
30
40
50
60
70
1996 1998 2000 2002 2004 2006 2008 2010 2012
Lending Rates (lr)
Inter-Bank Money Market Rates (ibr)
Len
din
g a
nd
In
ter-
ban
k R
ates
(%
)
Period of high interest rates volatility:
stdv (lr) = 9.21
stdv (ibr) = 13.3
Positive correlation:
r = 0.66
Low interest rates volatility era:
stdv (lr) = 4.42
stdv (ibr) = 3.60
Negative correlation:
r = - 0.24
Following Mishra et al. (2010) we investigate the relationship between the interbank and the
lending rate by adopting the following model:
where, lr is the lending rate and ibr is the interbank rate. Here the short-term is effect is given
by while the long term effect is given by (
The results of this estimation are summarized in Table 2 where they are also compared to the
findings for other groups of countries obtained from Mishra et al. (2010).2 While the
estimations for Zambia suggests the presence of positive long-run effects going beyond the
short-term impact, both the short-term and long-term effects are much smaller than in the
other country groupings. In Zambia, based on the full sample period, a one percentage point
2 See Appendix 2 for the detailed estimation results.
14
increase in the interbank causes the lending rate to increase by 2 basis points in the short run
and 13 basis points in the long run, much less than the corresponding 37 and 58 basis point
increases seen for emerging markets as a whole. Moreover, consistent with the correlations
identified in Figure 4, both the short and long-run effects in Zambia are found to have been
smaller in the period since 2006 than in the preceding period.
Table 2: Interest Rates Pass-through from Interbank Rates to Lending Rates
Description Short Run
Effects
Long Run
Effects
R-Squared
Advanced Economies 0.20 0.36 0.41
Emerging Markets 0.37 0.58 0.65
LICs 0.10 0.30 0.16
Zambia: Jan 1995 – Oct 2013
Jan 1995 – Jan 2006
Feb 2006 – Oct 2013
0.02 0.13 0.20
0.02 0.13 0.21
-0.01 0.04 0.16
Source: Mishra et al. (2010) and author’s computations,
The results in Table 2 highlight the challenge in moving to a monetary policy framework
where the policy rate is via the interbank rate meant to influence the wider Zambian
economy. Based on this evidence, monetary policymakers should, even with full control over
the interbank rate, not expect to have much control over broader macroeconomic outcomes
unless the interbank rate becomes more effective at determining lending rates than has been
the case so far.
B. Broader Linkages between Variables in Monetary Policy Transmission
Granger Causality Tests
As a first step to uncovering the wider set of variables that may be important for the MTM in
Zambia we perform pair-wise Granger causality tests. The Granger (1969) approach to the
question of whether X causes Y is to see how much of the current value of Y can be
explained by past values of Y and then to see whether adding lagged values of X can
improve the explanation. Here, Y is said to be Granger-caused by X if X helps in the
prediction of Y, or equivalently if the coefficients on the lagged values of X are statistically
significant. Specifically, the null hypothesis is that X does not cause Y and if the null
hypothesis is rejected then it implies that X may cause Y.
The results for the Granger causality test are summarized in Tables 3 and 4, with the full
estimation details shown in Appendix 3. The following patterns in interrelationships emerge
in relation to interest rates and monetary aggregates—the variables that are most closely
related to monetary policy:
15
Links between the policy rate and other interest rates. There is only weak evidence of
causality running from the policy rate to any of the other interest rates or from any of
these other rates to the policy rate. The results in Table 3 suggest possible causality
from the policy rate to the T-bill rate and from the interbank and deposit rates to the
policy rate, but these are all only significant at the 10 percent level. An important
limitation here, however, is that these tests are based on a very short sample size
starting in April 2012 when the policy rate was introduced.
Table 3: Pairwise Granger Causality Tests: Policy rate
Policy Rate
Y \ X (X causes Y) (Y causes X)
T-Bill Rate *
Interbank Rate *
Lending Rate
Deposit Rate *
Source: Author's calculations.
Note: "*", "**", and "***"indicate Granger Causality at significance level
of, respectively, 10, 5 and 1 percent.
Links from market interest rates to other variables. The results in Table 4 suggest that
the different market interest rates all influence each other, except the T-bill rate,
which does not seem to be influenced by any of the other interest rates, and the
interbank rate, which does not appear influenced by the deposit rate. Moreover, none
of the market interest rates appear to have a direct causal impact on reserve or broad
money, inflation, or output as proxied by electricity generation. In contrast, all the
market interest rates appear to have a significant impact on credit to the private
sector, and the T-bill and interbank rates also appear to influence the NEER.
Links to short-term interest rates from other variables. Evidence of causality running
to market interest rates depends on the interest rate in question. The interbank rate
appears to be influenced by all the other variables under consideration, but results for
the other interest rates are sporadic. However, the findings suggest that all the
different market interest rates are influenced by inflation as well as by credit to the
private sector, and also that broad money influences the T-bill as well as the interbank
rate.
Links from monetary aggregates to and from other variables. Broad money is found
to influence all variables except lending and deposit rates, while reserve money only
influences the interbank rate, inflation, and electricity generation. Reserve money is
influenced by broad money and output (as proxied by electricity generation), but
broad money is not influenced by any other variable.
16
Other links to inflation and output. Inflation is influenced by the NEER, credit to the
private sector, and output (as proxied by electricity generation). Moreover, output is
influenced by reserve and broad money as well as credit to the private sector.
Table 4: Pairwise Granger Causality Tests: Full Sample
(X Granger-Causes Y)
Y \ X (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
T-Bill Rate (1) - * ** *
Interbank Rate (2) *** - ** *** *** *** *** *** **
Lending Rate (3) *** ** - *** *** *
Deposit Rate (4) *** *** *** - *** ***
Reserve Money (5) - *** *
Broad Money (6) -
Inflation (7) ** *** - ** *** **
NEER (8) ** *** * ** -
Credit to Private Sector (9) ** *** *** *** * * -
Electricity Generation (10) * *** ** -
Source: Author's calculations.
Note: "*", "**", and "***"indicate Granger Causality at significance level of, respectively, 10, 5 and 1 percent.
The above findings enable some preliminary conclusions about the MTM channels in
Zambia. First, in as far as monetary policy works though interest rates, it appears to impact
the goal variables of inflation and output only via the exchange rate and credit to the private
sector. Second, where monetary policy works through reserve money, it appears to influence
inflation and output directly as well as indirectly via the interbank rate. The next section
looks further into these linkages and also examines their magnitudes.
VAR Model Estimation
Following widely used practice (Mishra et al., 2010;, Davoodi et al.,2013; Mishra and
Montiel, 2013), we assume that the impact of monetary policy on the wider economy van be
modeled in the following VAR structural form framework:
.
Here, is a nx1 vector of endogenous variables, c is a nx1 vector of constants, is a
mx1vector of exogenous variables, and is a nx1vector of error terms. A and B are nxn
and nxm matrices, which give the structure of the relationship among the endogenous and
exogenous variables in the model.
In our baseline model (Model 1), the endogenous variables are electricity production,
consumer price index (CPI), broad money (M3), interbank interest rate, and NEER. In
addition, following Sims (1992), the model includes a series of exogenous variables to
17
capture their direct impact on the economy as well as their possible influence on monetary
policy, namely the US federal funds rate, the oil price, and the index of international
commodity prices. We estimate the VAR on monthly data in log levels with 2 lags (as
determined by using lag selection criteria).
The VAR is used to investigate the impact of changes in monetary policy on the rest of the
economy by application of impulse-response and variance decomposition analysis. The
impulse response function traces the response of a variable to innovations in another variable,
and here we focus on shocks to monetary aggregates or short-term interest rates to discern
monetary policy action. The variance decomposition measures the amount of variation in a
given variable that is explained by variation in another variable at a given horizon. This gives
an indication of the degree that changes in variables closely tied to monetary policy influence
other macroeconomic variables.
The ordering of the endogenous variables in a VAR may have important bearing on the
results. We order electricity production first, on the assumption that real economic activity
responds sluggishly to policy and economic shocks. Next in the ordering is CPI, which is
assumed to respond contemporaneously to innovations in real economic activity but not in
the same period to innovations in the other variables. After that comes broad money to
indicate that it responds to prices and real economic activity. The interbank rate is ordered
after broad money to reflect that the central bank when intervening in the money market
looks to movements in broad money as well as inflation and real economic activity. The
NEER is ordered last, meaning that it responds contemporaneously to all the variables in the
model, reflecting the view that exchange rates respond more readily to changing economic
conditions than any other variable in the model. This ordering follows that used by Davoodi,
Dixit and Pinter (2013), Cheng (2006), and Buigut (2009).
To further identify the channels in the MTM and also to check the robustness of the results of
the baseline model, we examine a number of different variations to Model 1.
Model 2 uses reserve money instead of broad money, to better capture what is under
central bank control.
Model 3 includes private sector credit to evaluate the credit channel, placing it after
the short term interest rate and before the exchange rate in the variable ordering.
Model 4 retains the private credit variable but replaces broad money with reserve
money.
Model 5 includes the lending rate ordered after the T-bill rate to further investigate
the interest rate channel.
18
Model 6 uses quarterly instead of monthly data allowing us to replace electricity with
industrial production as a proxy for economic activity, albeit on a shorter sample size
spanning 2001Q1 to 2013Q3.
In addition to the different specifications above, we also re-estimate Model 1 over two
periods, 1995M1-2006M1 and 2006M2-2013M10 to investigate if the transmission
mechanism has changed over time.
Results
The impulse-response analysis for Model 1 is shown in Figure 5. The results indicate that a
positive shock to broad money has a statistically significant positive effect on output (as
proxied by electricity generation), consumer prices, and the NEER (meaning depreciation).
This is all as could be expected from expansionary monetary policy. There is with a short lag,
however, also a marginally significant positive impact on the interbank rate. This last effect
is not necessarily what could be expected but may reflect that greater money supply tends to
be followed by tightening of interest rate policy to counter pressure on the exchange rate and
consumer prices.
The impact of a positive shock to the interbank rate leads is much weaker than that of a shock
to money supply, with no significant impact on any of the other variables except for a
borderline significant appreciation of the NEER. This emphasizes the relative importance of
the exchange rate channel and is in line with the Granger causality results in Table 4, which
also did not uncover any significant impact of the interbank rate directly on output or
inflation.
The finding of a more powerful role for money supply than for interest rates in the MTM is
confirmed in the magnitude of the impulse responses. A one percent increase in M3 produces
within a time period of less than 6 months an approximately 0.6 percent depreciation, a 0.4
percent increase in output, and a 0.3 percentage point increase in the interbank rate, with the
impacts then fading out. The impact on CPI is more gradual and takes about 15 months to
reach 0.15 percent, but this effect persists. The impact of higher interest rates is much
weaker, with the effect of a one percentage point increase in the interbank rate on the NEER
peaking at about 0.15 percent within a few months.
Table 5 shows that shocks to broad money explain, respectively, 4 and 23 percent of the
forecast error variance of output and prices at the 36-month horizon. The corresponding
numbers for shocks to the interbank rate are 4 and 5 percent, respectively. This again
highlights how broad money is, or at least has been, far more important for inflation
outcomes in Zambia than is the interbank rate, and that neither has much bearing on changes
in output.
19
Figure 5: Model 1: Impulse Responses
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LELECTRICITYG to LM3
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LELECTRICITYG to INTERBANKR
-.008
-.004
.000
.004
.008
.012
5 10 15 20 25 30 35
Response of LCPI to LM3
-.008
-.004
.000
.004
.008
.012
5 10 15 20 25 30 35
Response of LCPI to INTERBANKR
-.01
.00
.01
.02
.03
.04
5 10 15 20 25 30 35
Response of LM3 to LM3
-.01
.00
.01
.02
.03
.04
5 10 15 20 25 30 35
Response of LM3 to INTERBANKR
-2
0
2
4
6
8
5 10 15 20 25 30 35
Response of INTERBANKR to LM3
-2
0
2
4
6
8
5 10 15 20 25 30 35
Response of INTERBANKR to INTERBANKR
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LNEER to LM3
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LNEER to INTERBANKR
Response to Cholesky One S.D. Innovations ± 2 S.E.
20
Table 5: Five Variables VAR-Variance Decomposition Variance Decomposition of LELECTRICITYG:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LNEER 1 0.064412 100.0000 0.000000 0.000000 0.000000 0.000000
6 0.099237 92.07292 0.428047 4.327663 3.114020 0.057350 12 0.106138 90.68329 0.518300 4.381233 4.055932 0.361249 24 0.108353 89.81301 0.744872 4.306966 4.346807 0.788348 36 0.108808 89.50973 0.928751 4.280761 4.401548 0.879211
Variance Decomposition of LCPI:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LNEER 1 0.010230 3.204355 96.79564 0.000000 0.000000 0.000000
6 0.029812 13.49318 78.96202 5.613721 1.663329 0.267751 12 0.041149 15.59236 69.90031 10.52548 3.699704 0.282132 24 0.055545 12.10843 64.70947 18.29044 4.676208 0.215450 36 0.066424 9.302950 62.77269 23.13447 4.637126 0.152760
Variance Decomposition of LM3:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LNEER 1 0.035382 0.065766 0.252632 99.68160 0.000000 0.000000
6 0.067265 11.13501 3.958818 84.23527 0.417101 0.253797 12 0.082614 21.93775 3.110156 73.94242 0.328075 0.681598 24 0.094499 23.61895 6.758055 67.78651 0.301039 1.535446 36 0.100644 21.42267 11.21952 65.28756 0.342444 1.727817
Variance Decomposition of INTERBANKR:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LNEER 1 6.185883 3.718053 6.88E-07 0.142989 96.13896 0.000000
6 7.119853 4.393583 4.878136 4.917510 85.51542 0.295346 12 7.218255 4.601294 5.687775 5.748075 83.31346 0.649400 24 7.323894 6.196782 5.953787 5.839788 81.06503 0.944613 36 7.350374 6.596998 6.028257 5.802492 80.55466 1.017598
Variance Decomposition of LNEER:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LNEER 1 0.037947 1.123474 1.058623 16.49691 0.165800 81.15519
6 0.089239 5.363571 2.036543 16.61232 4.161282 71.82629 12 0.100089 7.735939 1.809886 15.00787 5.213176 70.23312 24 0.106470 10.78012 3.719197 14.00846 5.881308 65.61091 36 0.109490 11.47546 6.021589 14.09785 6.060299 62.34480
21
The estimations relating to the various model extensions and robustness checks are shown in
Appendix 4, with the main differences highlighted here3,4:
Model 2 (Figure A1, Table A6), where reserve money is used instead of broad
money, shows that shocks to money supply no longer have a significant impact on
any of the other variables. This supports the results from the Granger-causality tests
indicating that broad money has a much stronger bearing on the economy than
reserve money.
Model 3 (Figure A2, Table A7), which includes credit to the private sector, retains the
same general results as Model 1 but, in addition, shows that credit to the private
sector is impacted positively by an increase in money supply. There is, however, no
significant impact on credit from an increase in the interbank rate.
Model 4 (Figure A3, Table A8), which retains the credit variable but replaces broad
money with reserve money, shows similar results to Model 2, with no significant
impact from shocks to reserve money to any of the other variables.
Model 5 (Figure A4, Table A9), which includes the lending as well as the T-bill rate,
shows that while both these interest rates have a (marginally) significant impact on
the NEER, only lending rates have a significant (and negative) impact on credit. That
lending rates are found to be more important for credit than interbank rates suggests
that borrowing is price sensitive but also confirms the limited transmission from the
interbank rate seen in the other model specifications.
Model 6 (Figure A5 Table A10), which uses industrial production instead of
electricity as a proxy for output, shows similar results to Model 1, except that the
significance of the results is generally somewhat weaker. In particular, the impact of
the interbank rate on the NEER is no longer significant. Moreover, the part of the
forecast error variance explained by other variables in the model is also mostly lower
than with Model 1. However, M3 is found to have greater influence on the variance in
industrial production (13 percent at the 3-year horizon) than it did on electricity
production in Model 1.
3 To check robustness of the variable ordering, we also tried by placing CPI after M3 (i.e., electricity
generation, M3, CPI, interbank rate, and nominal effective exchange rate). The results were not significantly
different from those obtained with Model 1.
4 We also tried using copper prices instead of the broader international commodity price index. The results were
not significantly different from our main results except that the influence of the interbank rate fell slightly.
22
The results from estimating Model 1 over the two sub-periods (1995-2005 and 2006-2013)
are broadly similar to those for the whole sample period, with the main differences pointing
to a mostly diminishing role for broad money on the price level and a somewhat increasing
role for the interbank rate (Figures A6-7 and Tables A11-12). In particular, the variance
decomposition at the 36-month horizon shows that the influence of the interbank rate on the
other variables increased between the two sub-periods in all cases except for the output proxy
where the contribution of the interbank rate was essentially unchanged. Further, the
contribution of broad money to changes in other variables was in all cases higher in the
second period except for the price level where it reduced. The rising role of interest rates is
also visible in the impulse response of CPI to the interbank rate, which for the second sub-
period shows an initial—albeit only borderline significant—decline in inflation.
VI. CONCLUSIONS
The evolving Zambian economy poses new challenges for the conduct of monetary policy. In
particular, as tighter control of money supply has helped bring inflation to single digits, the
previously strong link between consumer prices and reserve money has become less clear
cut. In response, the Bank of Zambia—along with many other central banks in similar
situations—is moving to a policy framework that targets inflation via a policy rate instead of
focusing on maintaining a certain growth rate in money aggregates.
In any policy framework, ensuring stability requires a good understanding of the monetary
transmission mechanism. The empirical evidence presented in this paper suggests that the
monetary transmission mechanism in Zambia has been weak and more closely connected to
monetary aggregates than interest rates. On the whole, and in contrast to money supply,
market interest rates do not appear to have had much direct bearing on either output or
inflation. Market interest rates do appear, however, to have important bearing on the
exchange rate and to some extent also on credit to the private sector. This all points to a
dominant role of the exchange rate channel in Zambia’s MTM, with lesser roles for the
interest and credit channels.5
While there is evidence to suggest that interest rates are gaining in importance compared to
monetary aggregates in Zambia’s MTM, especially with regards to inflation, it still appears
too early to abandon the traditional policy focus on monetary aggregates. While interest rates
appear to have gained in importance, based on available data one cannot conclude that
interest rates have become more important towards influencing macroeconomic outcomes
than monetary aggregates. A key implication from this review of the empirical evidence is
therefore that monetary policy in Zambia should continue to consider developments in
5 The presence of the expectations and asset price channels are harder to assess given limited data availability,
but these are unlikely to be that important in Zambia considering the relatively small size and basic nature of the
country’s financial sector.
23
monetary aggregates while gradually transitioning to inflation targeting. At the same time,
efforts to enhance the MTM, by promoting financial deepening and economic development
more generally, would assist in ensuring that monetary policy—through monetary aggregates
or via the policy rate—can continue to be effective in securing the overriding objective of
macroeconomic stability.
24
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Simatele, M. C. H., 2004, ―Financial Sector Reforms and Monetary Policy Reforms In
Zambia,‖ PhD Dissertation, University of Gothenburg, Sweden.
Sims, C. A., 1992, ―Interpreting the Macroeconomic Time Series Facts: the Effects of
Monetary Policy,‖ European Economic Review, Vol. 36 (June), pp. 975–1011.
Smal, M. M. and S. DeJager, 2001, ―The Monetary Transmission Mechanism in South
Africa,‖ South African Reserve Bank Occasional Paper No. 16.
Tahir, M. N., 2012, ―Relative Importance of Monetary Transmission Channels: A Structural
Investigation: the case of Brazil, Chile and Korea,‖ University of Lyon Working Paper
(Lyon: University of Lyon).
Taylor, J. B., 1993, ―Discretion versus Policy Rules in Practice,‖ Carnegie Rochester
Conference Series on Public Policy, 39, pp. 195–214.
Taylor, J. B., 1995, ―The Monetary Transmission Mechanism: An Empirical Framework,‖
Journal of Economic Perspectives, Vol.9, No.4, pp.11-26.
29
APPENDIX 1:
The study used the following data.
Description Frequency Source Span
Policy rate
Bank of Zambia policy
rate, in percent
Monthly Bank of
Zambia
April 2012 –
October 2013
T-bill rate Monthly average of 91-
day Treasury Bill yield,
in percent
Monthly Bank of
Zambia
January 1994-
October 2013
Interbank rate Monthly average
overnight interest rate at
which commercial
banks lend to each other
in the money market.
Monthly Bank of
Zambia
January 1994-
October 2013
Deposit rate
Monthly weighted
average of interest rates
on commercial bank
deposits.
Monthly Bank of
Zambia
January 1994-
October 2013
Lending rate
Monthly weighted
average of interest rates
on commercial bank
loans
Monthly Bank of
Zambia
January 1994-
October 2013
Reserve money Monthly average of total
liquid assets of
commercial banks plus
currency in circulation.
Monthly Bank of
Zambia
January 1994-
October 2013
Broad money Monthly average of
(M3) money supply
including foreign
currency deposits.
Monthly Bank of
Zambia
January 1994-
October 2013
Consumer prices
Consumer price index. Monthly Central
Statistical
Office
January 1994-
October 2013
Nominal effective
exchange rate
Monthly average
Kwacha/US dollar
exchange rate.
Monthly Bank of
Zambia
January 1994-
October 2013
Credit to the
private sector
Total commercial bank
credit to the private
sector.
Monthly Bank of
Zambia
January 1994-
October 2013
Electricity
production
Total production of
electricity in kWh .
Monthly ZESCO January 1994-
October 2013
30
Industrial
production
Index of industrial
production.
Quarterly Central
Statistical
Office
2001Q1-
2013Q2
Federal Funds
rate
US Federal Funds Rate. Monthly Federal
Reserve Bank
of New York
Website
January 1994-
October 2013
Oil price US dollar price of crude
oil on the international
market.
Monthly IMF January 1994-
October 2013
Commodity price Index of world
commodity prices.
Monthly IMF January 1994-
October 2013
31
APPENDIX 2
Detailed estimation results behind Table 2.
Table A1: Lending Rates and Interbank Rate Estimations, Entire Sample Period
Dependent Variable: D(LR)
Method: Least Squares
Sample (adjusted): 1995M04 2013M10
Included observations: 223 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
D(LR(-1)) 0.337854 0.065645 5.146715 0.0000
D(LR(-2)) 0.107462 0.065492 1.640839 0.1023
D(IBR) 0.016010 0.011059 1.447650 0.1492
D(IBR(-1)) 0.025599 0.012479 2.051421 0.0414
D(IBR(-2)) 0.031131 0.011041 2.819574 0.0053
R-squared 0.201289 Mean dependent var -0.153687
Adjusted R-squared 0.186633 S.D. dependent var 1.303117
S.E. of regression 1.175240 Akaike info criterion 3.182988
Sum squared resid 301.0993 Schwarz criterion 3.259382
Log likelihood -349.9032 Hannan-Quinn criter. 3.213828
Durbin-Watson stat 2.038584
Table A2: Lending Rates and Interbank Rate Estimations, High Interest Rate Volatility
Era
Dependent Variable: D(LR)
Method: Least Squares
Sample (adjusted): 1995M04 2006M01
Included observations: 130 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
D(LR(-1)) 0.332858 0.086523 3.847063 0.0002
D(LR(-2)) 0.103943 0.086335 1.203954 0.2309
D(IBR) 0.016925 0.014052 1.204453 0.2307
D(IBR(-1)) 0.026715 0.015984 1.671389 0.0971
D(IBR(-2)) 0.031654 0.014084 2.247509 0.0264
R-squared 0.206912 Mean dependent var -0.135385
Adjusted R-squared 0.181533 S.D. dependent var 1.623421
S.E. of regression 1.468695 Akaike info criterion 3.644329
Sum squared resid 269.6333 Schwarz criterion 3.754618
Log likelihood -231.8814 Hannan-Quinn criter. 3.689143
Durbin-Watson stat 2.044697
32
Table A3: Lending Rates and Interbank Rate Estimations, Low Interest Rate Volatility
Era
Dependent Variable: D(LR)
Sample: 2006M02 2013M10
Included observations: 93
Variable Coefficient Std. Error t-Statistic Prob.
D(LR(-1)) 0.380517 0.104752 3.632572 0.0005
D(LR(-2)) 0.134533 0.104473 1.287737 0.2012
D(IBR) -0.013500 0.039666 -0.340343 0.7344
D(IBR(-1)) 0.011658 0.032003 0.364265 0.7165
D(IBR(-2)) 0.019300 0.030885 0.624896 0.5337
R-squared 0.158837 Mean dependent var -0.179271
Adjusted R-squared 0.120602 S.D. dependent var 0.633296
S.E. of regression 0.593881 Akaike info criterion 1.847989
Sum squared resid 31.03714 Schwarz criterion 1.984150
Log likelihood -80.93149 Hannan-Quinn criter. 1.902967
Durbin-Watson stat 1.979202
33
APPENDIX 3
Detailed estimation results behind Tables 3 and 4.
Table A4: Pairwise Granger Causality with Policy Rate, 2012M4-2013M9
Lags: 2 Null Hypothesis: Obs F-Statistic Prob. TB91 does not Granger Cause POLICYR 19 0.85324 0.4470
POLICYR does not Granger Cause TB91 2.86591 0.0905 INTERBANKR does not Granger Cause POLICYR 19 3.36170 0.0642
POLICYR does not Granger Cause INTERBANKR 0.10697 0.8993 LENDINGR does not Granger Cause POLICYR 19 0.52247 0.6042
POLICYR does not Granger Cause LENDINGR 1.17497 0.3375 DEPR does not Granger Cause POLICYR 19 3.58998 0.0551
POLICYR does not Granger Cause DEPR 0.02850 0.9720 INTERBANKR does not Granger Cause TB91 19 0.50126 0.6162
TB91 does not Granger Cause INTERBANKR 5.39090 0.0184 LENDINGR does not Granger Cause TB91 19 0.73673 0.4963
TB91 does not Granger Cause LENDINGR 0.04928 0.9521 DEPR does not Granger Cause TB91 19 0.57896 0.5734
TB91 does not Granger Cause DEPR 2.46116 0.1214 LENDINGR does not Granger Cause INTERBANKR 19 2.34996 0.1318
INTERBANKR does not Granger Cause LENDINGR 1.59251 0.2381 DEPR does not Granger Cause INTERBANKR 19 8.67888 0.0035
INTERBANKR does not Granger Cause DEPR 1.00094 0.3924 DEPR does not Granger Cause LENDINGR 19 0.65180 0.5362
LENDINGR does not Granger Cause DEPR 0.33832 0.7186
Table A5: Granger Causality Tests, 1995M1-2013M9
Lags: 2 Null Hypothesis: Obs F-Statistic Prob. INTERBANKR does not Granger Cause TB91 200 1.83770 0.1619
TB91 does not Granger Cause INTERBANKR 7.87630 0.0005 LENDINGR does not Granger Cause TB91 200 0.20735 0.8129
TB91 does not Granger Cause LENDINGR 16.9509 2.E-07 DEPR does not Granger Cause TB91 200 1.03844 0.3560
TB91 does not Granger Cause DEPR 20.2004 1.E-08 LM0 does not Granger Cause TB91 200 2.13922 0.1205
TB91 does not Granger Cause LM0 0.00335 0.9967
34
LM3 does not Granger Cause TB91 200 2.44641 0.0893
TB91 does not Granger Cause LM3 1.54630 0.2156 D12LCPI does not Granger Cause TB91 200 4.43377 0.0131
TB91 does not Granger Cause D12LCPI 0.22374 0.7997 LNEER does not Granger Cause TB91 200 0.74370 0.4767
TB91 does not Granger Cause LNEER 3.48584 0.0326 LCREDIT does not Granger Cause TB91 200 2.66062 0.0724
TB91 does not Granger Cause LCREDIT 3.14474 0.0453 LELECTRICITYG does not Granger Cause TB91 200 1.17989 0.3095
TB91 does not Granger Cause LELECTRICITYG 0.97604 0.3786 LENDINGR does not Granger Cause INTERBANKR 200 4.62804 0.0109
INTERBANKR does not Granger Cause LENDINGR 4.66070 0.0105 DEPR does not Granger Cause INTERBANKR 200 2.22152 0.1112
INTERBANKR does not Granger Cause DEPR 9.08061 0.0002 LM0 does not Granger Cause INTERBANKR 200 6.00062 0.0030
INTERBANKR does not Granger Cause LM0 1.63258 0.1981 LM3 does not Granger Cause INTERBANKR 200 6.75087 0.0015
INTERBANKR does not Granger Cause LM3 0.88630 0.4138 D12LCPI does not Granger Cause INTERBANKR 200 18.1865 6.E-08
INTERBANKR does not Granger Cause D12LCPI 0.10245 0.9027 LNEER does not Granger Cause INTERBANKR 200 6.96489 0.0012
INTERBANKR does not Granger Cause LNEER 5.42582 0.0051 LCREDIT does not Granger Cause INTERBANKR 200 5.63106 0.0042
INTERBANKR does not Granger Cause LCREDIT 5.36935 0.0054 LELECTRICITYG does not Granger Cause INTERBANKR 200 4.34134 0.0143
INTERBANKR does not Granger Cause LELECTRICITYG 0.97724 0.3782 DEPR does not Granger Cause LENDINGR 200 5.81248 0.0035
LENDINGR does not Granger Cause DEPR 8.71049 0.0002 LM0 does not Granger Cause LENDINGR 200 1.40454 0.2480
LENDINGR does not Granger Cause LM0 1.71107 0.1834 LM3 does not Granger Cause LENDINGR 200 1.61511 0.2015
LENDINGR does not Granger Cause LM3 0.30946 0.7342 D12LCPI does not Granger Cause LENDINGR 200 13.5274 3.E-06
LENDINGR does not Granger Cause D12LCPI 0.10460 0.9007 LNEER does not Granger Cause LENDINGR 200 0.11923 0.8877
LENDINGR does not Granger Cause LNEER 0.94607 0.3900 LCREDIT does not Granger Cause LENDINGR 200 2.38002 0.0952
LENDINGR does not Granger Cause LCREDIT 7.07877 0.0011 LELECTRICITYG does not Granger Cause LENDINGR 200 0.34649 0.7076
LENDINGR does not Granger Cause LELECTRICITYG 1.73800 0.1786 LM0 does not Granger Cause DEPR 200 1.97469 0.1416
DEPR does not Granger Cause LM0 0.32438 0.7234
35
LM3 does not Granger Cause DEPR 200 2.22851 0.1104
DEPR does not Granger Cause LM3 0.11840 0.8884 D12LCPI does not Granger Cause DEPR 200 8.33866 0.0003
DEPR does not Granger Cause D12LCPI 0.92552 0.3981 LNEER does not Granger Cause DEPR 200 0.38329 0.6821
DEPR does not Granger Cause LNEER 0.86006 0.4247 LCREDIT does not Granger Cause DEPR 200 5.15130 0.0066
DEPR does not Granger Cause LCREDIT 10.2099 6.E-05 LELECTRICITYG does not Granger Cause DEPR 200 0.47772 0.6209
DEPR does not Granger Cause LELECTRICITYG 1.72442 0.1810 LM3 does not Granger Cause LM0 200 15.1341 8.E-07
LM0 does not Granger Cause LM3 0.39356 0.6752 D12LCPI does not Granger Cause LM0 200 0.51302 0.5995
LM0 does not Granger Cause D12LCPI 4.40372 0.0135 LNEER does not Granger Cause LM0 200 1.37782 0.2546
LM0 does not Granger Cause LNEER 1.97352 0.1417 LCREDIT does not Granger Cause LM0 200 1.41836 0.2446
LM0 does not Granger Cause LCREDIT 1.42810 0.2423 LELECTRICITYG does not Granger Cause LM0 200 2.87420 0.0589
LM0 does not Granger Cause LELECTRICITYG 3.03905 0.0502 D12LCPI does not Granger Cause LM3 200 1.81721 0.1652
LM3 does not Granger Cause D12LCPI 6.37195 0.0021 LNEER does not Granger Cause LM3 200 0.36440 0.6951
LM3 does not Granger Cause LNEER 2.64480 0.0736 LCREDIT does not Granger Cause LM3 200 0.81167 0.4456
LM3 does not Granger Cause LCREDIT 2.54533 0.0811 LELECTRICITYG does not Granger Cause LM3 200 1.36814 0.2570
LM3 does not Granger Cause LELECTRICITYG 8.97522 0.0002 LNEER does not Granger Cause D12LCPI 200 4.63597 0.0108
D12LCPI does not Granger Cause LNEER 3.57211 0.0299 LCREDIT does not Granger Cause D12LCPI 200 5.52004 0.0047
D12LCPI does not Granger Cause LCREDIT 2.65899 0.0726 LELECTRICITYG does not Granger Cause D12LCPI 200 3.05637 0.0493
D12LCPI does not Granger Cause LELECTRICITYG 1.08788 0.3390 LCREDIT does not Granger Cause LNEER 200 1.57634 0.2094
LNEER does not Granger Cause LCREDIT 0.60151 0.5490 LELECTRICITYG does not Granger Cause LNEER 200 1.49458 0.2269
LNEER does not Granger Cause LELECTRICITYG 1.91825 0.1496 LELECTRICITYG does not Granger Cause LCREDIT 200 0.80622 0.4480
LCREDIT does not Granger Cause LELECTRICITYG 4.65824 0.0106
36
APPENDIX 4
Figure A1: Model 2-Impulse Response
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LELECTRICITYG to LM0
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LELECTRICITYG to INTERBANKR
-.008
-.004
.000
.004
.008
5 10 15 20 25 30 35
Response of LCPI to LM0
-.008
-.004
.000
.004
.008
5 10 15 20 25 30 35
Response of LCPI to INTERBANKR
-.02
.00
.02
.04
.06
5 10 15 20 25 30 35
Response of LM0 to LM0
-.02
.00
.02
.04
.06
5 10 15 20 25 30 35
Response of LM0 to INTERBANKR
-2
0
2
4
6
8
5 10 15 20 25 30 35
Response of INTERBANKR to LM0
-2
0
2
4
6
8
5 10 15 20 25 30 35
Response of INTERBANKR to INTERBANKR
-.02
-.01
.00
.01
.02
5 10 15 20 25 30 35
Response of LNEER to LM0
-.02
-.01
.00
.01
.02
5 10 15 20 25 30 35
Response of LNEER to INTERBANKR
Response to Cholesky One S.D. Innovations ± 2 S.E.
37
Table A6: Model 2-Variance Decomposition
Variance Decomposition of LELECTRICITYG:
Period S.E. LELECTRICI
TYG LCPI LM0 INTERBANK
R LNEER 1 0.065838 100.0000 0.000000 0.000000 0.000000 0.000000
6 0.106766 95.37550 1.586901 1.665285 1.122957 0.249360 12 0.111482 93.75026 1.484071 1.750769 2.495974 0.518930 24 0.111859 93.26764 1.528887 1.747562 2.674353 0.781558 36 0.111886 93.22426 1.551616 1.747402 2.673858 0.802869
Variance Decomposition of LCPI:
Period S.E. LELECTRICI
TYG LCPI LM0 INTERBANK
R LNEER 1 0.010273 2.182492 97.81751 0.000000 0.000000 0.000000
6 0.031235 8.845017 83.85221 1.840590 0.765237 4.696945 12 0.043176 9.149851 82.88137 2.479994 1.844373 3.644407 24 0.058750 7.352357 84.42271 2.613020 2.797673 2.814238 36 0.070068 6.577456 85.31866 2.597971 3.071335 2.434577
Variance Decomposition of LM0:
Period S.E. LELECTRICI
TYG LCPI LM0 INTERBANK
R LNEER 1 0.051764 3.579816 0.461511 95.95867 0.000000 0.000000
6 0.083497 27.95309 1.580535 65.14438 3.078041 2.243956 12 0.095676 38.47934 3.217174 52.39854 2.841492 3.063451 24 0.106063 32.97108 17.42498 43.80502 2.466062 3.332866 36 0.117061 27.59987 30.37561 36.51068 2.504209 3.009622
Variance Decomposition of INTERBANKR:
Period S.E. LELECTRICI
TYG LCPI LM0 INTERBANK
R LNEER 1 6.139427 2.427775 0.122274 0.161415 97.28854 0.000000
6 7.591180 2.092713 9.181387 0.463995 86.44598 1.815923 12 7.648964 2.249487 9.055498 0.505905 85.90830 2.280810 24 7.656275 2.250872 9.043050 0.508756 85.74873 2.448592 36 7.657535 2.251128 9.057789 0.511620 85.72113 2.458333
Variance Decomposition of LNEER:
Period S.E. LELECTRICI
TYG LCPI LM0 INTERBANK
R LNEER 1 0.038568 0.850065 1.949324 0.000642 0.154880 97.04509
6 0.089753 7.082571 1.523902 0.217908 3.106110 88.06951 12 0.100709 7.829391 1.223303 1.111199 2.670841 87.16526 24 0.104390 7.419784 1.838048 1.855521 2.539219 86.34743 36 0.105983 7.226656 3.978741 2.023404 2.512895 84.25830
38
Varian
ce Decompositio
n of LELECTRICIT
YG:
Period S.E. LELECTRICI
TYG LCPI LM0 INTERBANK
R LNEER 1 0.065838 100.0000 0.000000 0.000000 0.000000 0.000000
2 0.077955 98.22581 0.000151 0.900692 0.733174 0.140170 3 0.092096 97.29975 0.833300 1.201505 0.528895 0.136548 4 0.098800 96.39196 1.352841 1.386524 0.743687 0.124990 5 0.103850 96.01205 1.501022 1.579924 0.731402 0.175606 6 0.106766 95.37550 1.586901 1.665285 1.122957 0.249360 7 0.108678 95.06579 1.546634 1.710644 1.361845 0.315089 8 0.109854 94.62357 1.526527 1.732184 1.758637 0.359080 9 0.110590 94.33276 1.506698 1.742027 2.020587 0.397927
10 0.111035 94.07707 1.495266 1.747703 2.244757 0.435208 11 0.111311 93.89408 1.487925 1.749879 2.391804 0.476314 12 0.111482 93.75026 1.484071 1.750769 2.495974 0.518930 13 0.111593 93.64010 1.483341 1.750610 2.565014 0.560939 14 0.111667 93.55424 1.485183 1.750131 2.610708 0.599739 15 0.111718 93.48810 1.488718 1.749572 2.639359 0.634252 16 0.111753 93.43682 1.493224 1.749071 2.656634 0.664254 17 0.111780 93.39689 1.498151 1.748664 2.666306 0.689988 18 0.111800 93.36527 1.503170 1.748343 2.671418 0.711800 19 0.111815 93.33989 1.508130 1.748096 2.673826 0.730059 20 0.111828 93.31928 1.512891 1.747909 2.674786 0.745135 21 0.111838 93.30242 1.517392 1.747771 2.674996 0.757426 22 0.111846 93.28855 1.521568 1.747673 2.674868 0.767339 23 0.111853 93.27711 1.525401 1.747606 2.674616 0.775264 24 0.111859 93.26764 1.528887 1.747562 2.674353 0.781558 25 0.111863 93.25977 1.532042 1.747535 2.674126 0.786528 26 0.111867 93.25321 1.534887 1.747519 2.673952 0.790433 27 0.111871 93.24773 1.537448 1.747509 2.673829 0.793487 28 0.111874 93.24313 1.539752 1.747501 2.673752 0.795866 29 0.111876 93.23926 1.541822 1.747494 2.673710 0.797711 30 0.111878 93.23600 1.543685 1.747486 2.673696 0.799138 31 0.111880 93.23322 1.545361 1.747476 2.673701 0.800237 32 0.111881 93.23086 1.546872 1.747464 2.673720 0.801080 33 0.111883 93.22884 1.548237 1.747451 2.673749 0.801725 34 0.111884 93.22709 1.549473 1.747436 2.673783 0.802216 35 0.111885 93.22558 1.550595 1.747419 2.673820 0.802588 36 0.111886 93.22426 1.551616 1.747402 2.673858 0.802869
Varian
ce Decompositio
n of LCPI:
Period S.E. LELECTRICI
TYG LCPI LM0 INTERBANK
R LNEER 1 0.010273 2.182492 97.81751 0.000000 0.000000 0.000000
2 0.016460 4.376240 93.72254 0.240243 0.403406 1.257576 3 0.021590 5.535173 89.44129 1.019561 1.107971 2.896001 4 0.025646 6.858251 86.88916 1.341634 0.817464 4.093490 5 0.028622 8.073755 84.96283 1.624543 0.663475 4.675399 6 0.031235 8.845017 83.85221 1.840590 0.765237 4.696945 7 0.033552 9.340385 83.17597 2.011319 0.949274 4.523049 8 0.035735 9.524377 82.87313 2.154826 1.157393 4.290277 9 0.037785 9.544320 82.75760 2.268088 1.349109 4.080885
10 0.039700 9.458079 82.75287 2.359355 1.520845 3.908848 11 0.041494 9.317841 82.80048 2.428176 1.687207 3.766293
Figure A2: Model 3-Impulse Responses
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LELECTRICITYG to LM3
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LELECTRICITYG to INTERBANKR
-.008
-.004
.000
.004
.008
.012
5 10 15 20 25 30 35
Response of LCPI to LM3
-.008
-.004
.000
.004
.008
.012
5 10 15 20 25 30 35
Response of LCPI to INTERBANKR
-.01
.00
.01
.02
.03
.04
5 10 15 20 25 30 35
Response of LM3 to LM3
-.01
.00
.01
.02
.03
.04
5 10 15 20 25 30 35
Response of LM3 to INTERBANKR
-2
0
2
4
6
8
5 10 15 20 25 30 35
Response of INTERBANKR to LM3
-2
0
2
4
6
8
5 10 15 20 25 30 35
Response of INTERBANKR to INTERBANKR
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LCREDIT to LM3
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LCREDIT to INTERBANKR
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LNEER to LM3
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LNEER to INTERBANKR
Response to Cholesky One S.D. Innovations ± 2 S.E.
39
Table A7: Model 3-Variance Decomposition
Variance Decomposition of LELECTRICITYG:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LCREDIT LNEER 1 0.063668 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000
12 0.102840 90.00095 1.233696 3.036357 2.686366 2.606213 0.436414 24 0.104465 87.45839 1.891811 2.963742 2.637146 4.374780 0.674132 36 0.104926 86.71333 2.067232 2.946649 2.614179 4.805109 0.853502
Variance Decomposition of LCPI:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LCREDIT LNEER 1 0.010229 2.985471 97.01453 0.000000 0.000000 0.000000 0.000000
12 0.041989 12.59992 71.85664 12.28289 2.479145 0.724951 0.056453 24 0.057176 8.085301 67.49934 20.22998 2.903362 1.246314 0.035698 36 0.068202 5.945797 65.70682 24.20480 2.929889 1.187285 0.025412
Variance Decomposition of LM3:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LCREDIT LNEER 1 0.035379 0.060887 0.312519 99.62659 0.000000 0.000000 0.000000
12 0.079255 20.23934 3.130735 74.18332 0.533857 1.705376 0.207369 24 0.087744 19.23896 6.405034 69.34201 0.651213 3.862212 0.500574 36 0.094157 16.71729 11.74576 65.60569 1.012393 4.043797 0.875070
Variance Decomposition of INTERBANKR:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LCREDIT LNEER 1 6.199045 3.598411 0.000212 0.141802 96.25957 0.000000 0.000000
12 7.218353 4.510600 5.769138 5.474883 83.54386 0.063325 0.638192 24 7.294285 5.466997 6.227668 5.470454 81.84549 0.289707 0.699687 36 7.312868 5.508069 6.381936 5.457550 81.43705 0.482003 0.733391
Variance Decomposition of LCREDIT:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LCREDIT LNEER 1 0.044118 0.003896 2.275127 11.98183 0.249993 85.48916 0.000000
12 0.094317 0.363528 4.357148 17.42004 1.569477 70.45658 5.833227 24 0.109463 0.722504 5.921535 20.25419 1.813760 62.40246 8.885549 36 0.120062 0.630955 10.88319 23.14046 2.055305 54.34060 8.949486
Variance Decomposition of LNEER:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LCREDIT LNEER 1 0.037635 0.725985 0.943117 17.35665 0.171309 1.030492 79.77244
12 0.093392 5.405879 2.352226 22.37297 3.778363 1.999686 64.09088 24 0.099375 5.075162 5.892687 22.18473 3.610213 4.354132 58.88308 36 0.102446 4.900054 8.672392 22.07453 3.497746 5.083608 55.77167
40
Figure A3: Model 4-Impulse Responses
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LELECTRICITYG to LM0
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LELECTRICITYG to INTERBANKR
-.012
-.008
-.004
.000
.004
.008
5 10 15 20 25 30 35
Response of LCPI to LM0
-.012
-.008
-.004
.000
.004
.008
5 10 15 20 25 30 35
Response of LCPI to INTERBANKR
-.02
.00
.02
.04
.06
5 10 15 20 25 30 35
Response of LM0 to LM0
-.02
.00
.02
.04
.06
5 10 15 20 25 30 35
Response of LM0 to INTERBANKR
-2
0
2
4
6
8
5 10 15 20 25 30 35
Response of INTERBANKR to LM0
-2
0
2
4
6
8
5 10 15 20 25 30 35
Response of INTERBANKR to INTERBANKR
-.015
-.010
-.005
.000
.005
.010
5 10 15 20 25 30 35
Response of LCREDIT to LM0
-.015
-.010
-.005
.000
.005
.010
5 10 15 20 25 30 35
Response of LCREDIT to INTERBANKR
-.03
-.02
-.01
.00
.01
.02
5 10 15 20 25 30 35
Response of LNEER to LM0
-.03
-.02
-.01
.00
.01
.02
5 10 15 20 25 30 35
Response of LNEER to INTERBANKR
Response to Cholesky One S.D. Innovations ± 2 S.E.
41
Table A8: Model 4-Variance Decomposition
Variance Decomposition of LELECTRICITYG:
Period S.E. LELECTRICI
TYG LCPI LM0 INTERBANK
R LCREDIT LNEER 1 0.064372 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000
12 0.106888 87.86076 4.145198 0.897948 3.116831 3.738909 0.240358
24 0.108279 85.73080 5.018709 0.878524 3.316413 4.723910 0.331648
36 0.108691 85.08643 5.229278 0.876956 3.319644 5.091651 0.396041 Variance Decomposition of LCPI:
Period S.E. LELECTRICI
TYG LCPI LM0 INTERBANK
R LCREDIT LNEER 1 0.010076 1.415596 98.58440 0.000000 0.000000 0.000000 0.000000
12 0.042303 7.640716 82.64464 1.724736 5.230246 0.587167 2.172499
24 0.059439 5.821457 83.32036 1.629383 7.297183 0.350751 1.580863
36 0.072262 5.203844 83.63174 1.579125 7.960814 0.270613 1.353866 Variance Decomposition of LM0:
Period S.E. LELECTRICI
TYG LCPI LM0 INTERBANK
R LCREDIT LNEER 1 0.051979 4.006111 0.338825 95.65506 0.000000 0.000000 0.000000
12 0.096554 38.43356 2.673692 52.05427 2.891471 2.013972 1.933037
24 0.105292 33.56149 13.57404 45.09824 2.831387 2.656154 2.278691
36 0.115553 28.26048 25.69776 37.84067 3.691704 2.432557 2.076832 Variance Decomposition of INTERBANKR:
Period S.E. LELECTRICI
TYG LCPI LM0 INTERBANK
R LCREDIT LNEER 1 6.132121 2.833814 0.006092 0.246265 96.91383 0.000000 0.000000
12 7.483428 3.427567 9.463756 0.626266 83.37109 2.060269 1.051048
24 7.494481 3.424403 9.524393 0.627380 83.12898 2.170704 1.124137
36 7.499448 3.419965 9.550414 0.628932 83.02151 2.237656 1.141522 Variance Decomposition of LCREDIT:
Period S.E. LELECTRICI
TYG LCPI LM0 INTERBANK
R LCREDIT LNEER 1 0.042195 0.170913 2.096540 0.087613 0.193065 97.45187 0.000000
12 0.088172 0.426880 1.964297 0.048618 0.257528 96.32677 0.975905
24 0.105619 1.555222 8.814889 0.091965 1.168058 86.62964 1.740223
36 0.119595 2.551661 19.06966 0.175363 2.501684 74.04335 1.658280 Variance Decomposition of LNEER:
Period S.E. LELECTRICI
TYG LCPI LM0 INTERBANK
R LCREDIT LNEER 1 0.038177 0.878195 2.302585 0.032691 0.190511 11.22919 85.36683
12 0.092804 3.589143 2.441774 0.453706 3.456765 13.48391 76.57470
24 0.098044 3.218453 7.778689 0.976777 3.419000 12.71720 71.88988
36 0.101922 3.017768 12.74537 1.129242 3.611641 12.38706 67.10892
42
Figure A4: Model 5-Impulse Responses
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LELECTRICITYG to LM3
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LELECTRICITYG to INTERBANKR
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LELECTRICITYG to LENDINGR
-.008
-.004
.000
.004
.008
.012
5 10 15 20 25 30 35
Response of LCPI to LM3
-.008
-.004
.000
.004
.008
.012
5 10 15 20 25 30 35
Response of LCPI to INTERBANKR
-.008
-.004
.000
.004
.008
.012
5 10 15 20 25 30 35
Response of LCPI to LENDINGR
-.02
-.01
.00
.01
.02
.03
.04
5 10 15 20 25 30 35
Response of LM3 to LM3
-.02
-.01
.00
.01
.02
.03
.04
5 10 15 20 25 30 35
Response of LM3 to INTERBANKR
-.02
-.01
.00
.01
.02
.03
.04
5 10 15 20 25 30 35
Response of LM3 to LENDINGR
-2
0
2
4
6
8
5 10 15 20 25 30 35
Response of INTERBANKR to LM3
-2
0
2
4
6
8
5 10 15 20 25 30 35
Response of INTERBANKR to INTERBANKR
-2
0
2
4
6
8
5 10 15 20 25 30 35
Response of INTERBANKR to LENDINGR
-0.4
0.0
0.4
0.8
1.2
1.6
5 10 15 20 25 30 35
Response of LENDINGR to LM3
-0.4
0.0
0.4
0.8
1.2
1.6
5 10 15 20 25 30 35
Response of LENDINGR to INTERBANKR
-0.4
0.0
0.4
0.8
1.2
1.6
5 10 15 20 25 30 35
Response of LENDINGR to LENDINGR
-.03
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LCREDIT to LM3
-.03
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LCREDIT to INTERBANKR
-.03
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LCREDIT to LENDINGR
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LNEER to LM3
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LNEER to INTERBANKR
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LNEER to LENDINGR
Response to Cholesky One S.D. Innovations ± 2 S.E.
43
Table A9: Model 5-Variance Decomposition
Variance Decomposition of LELECTRICITYG:
Period S.E. LELECTRICITYG LCPI LM3 INTERBANK LENDINGR LCREDIT LNEER 1 0.063711 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
12 0.103626 87.96071 0.846682 3.510709 2.493506 1.152527 3.366253 0.669616 24 0.105436 85.27063 1.668271 3.452482 2.522163 1.483565 4.514494 1.088392 36 0.106126 84.20201 1.990547 3.481658 2.493350 1.852216 4.519997 1.460218
Variance Decomposition of LCPI:
Period S.E. LELECTRICITYG LCPI LM3 INTERBANK LENDINGR LCREDIT LNEER 1 0.010222 2.906689 97.09331 0.000000 0.000000 0.000000 0.000000 0.000000
12 0.041688 11.92060 71.46125 11.14731 2.059707 1.158359 2.087408 0.165368 24 0.056233 7.795253 65.85306 17.99588 2.930757 1.694348 3.575342 0.155361 36 0.066403 5.792218 64.31078 21.51845 2.998992 1.586678 3.681309 0.111572
Variance Decomposition of LM3:
Period S.E. LELECTRICITY LCPI LM3 INTERBANK LENDINGR LCREDIT LNEER 1 0.035402 0.115754 0.326668 99.55758 0.000000 0.000000 0.000000 0.000000
12 0.078523 22.02056 3.537140 71.46321 0.430019 1.556548 0.781473 0.211058 24 0.085514 21.90609 5.886052 66.89420 0.542423 2.710657 1.211214 0.849354 36 0.090546 19.55601 9.699240 62.95236 0.978919 4.115187 1.148017 1.550270
Variance Decomposition of INTERBANKR:
Period S.E. LELECTRICITY LCPI LM3 INTERBANK LENDINGR LCREDIT LNEER 1 6.225507 3.463474 0.000748 0.124069 96.41171 0.000000 0.000000 0.000000
12 7.246456 4.291544 5.868128 5.494850 83.48290 0.148563 0.039389 0.674630 24 7.322204 5.263072 6.390036 5.456660 81.78968 0.159341 0.173023 0.768193 36 7.341111 5.313268 6.588120 5.469305 81.37864 0.179786 0.260283 0.810595
Variance Decomposition of LENDINGR:
Period S.E. LELECTRICITY LCPI LM3 INTERBANK LENDINGR LCREDIT LNEER 1 1.086073 0.719543 1.280791 0.846894 0.050676 97.10210 0.000000 0.000000
12 3.583920 3.017202 8.787854 0.677376 4.414551 77.54058 1.822737 3.739704 24 3.664095 2.951975 8.621733 0.685851 4.749248 76.32290 2.695478 3.972819 36 3.674143 2.945524 8.915851 0.751944 4.729901 75.92115 2.742246 3.993384
Variance Decomposition of LCREDIT:
Period S.E. LELECTRICITY LCPI LM3 INTERBANK LENDINGR LCREDIT LNEER 1 0.041793 0.160008 3.258819 11.60079 0.389594 0.457434 84.13336 0.000000
12 0.088008 0.464687 8.236450 11.21258 0.972612 20.40611 46.28008 12.42747 24 0.103560 1.234029 6.461705 9.715930 1.904318 28.42565 33.78411 18.47426 36 0.109317 1.256576 8.416763 10.60117 2.274419 28.61008 30.37337 18.46762
Variance Decomposition of LNEER:
Period S.E. LELECTRICITY LCPI LM3 INTERBANK LENDINGR LCREDIT LNEER 1 0.037696 0.640038 1.065227 17.05032 0.173354 0.363336 1.404415 79.30331
12 0.092986 5.245591 2.667503 20.57474 4.049148 3.862605 1.854563 61.74586 24 0.099470 4.964324 7.257297 20.70110 3.910843 3.645999 3.044487 56.47595 36 0.103096 4.797367 10.64690 20.96312 3.716871 3.582567 3.202655 53.09052
44
Figure A5: Model 6-Impulse Responses
-.008
-.004
.000
.004
.008
.012
1 2 3 4 5 6 7 8 9 10 11 12
Response of LIIP to LM3
-.008
-.004
.000
.004
.008
.012
1 2 3 4 5 6 7 8 9 10 11 12
Response of LIIP to INTERBANKR
-.004
.000
.004
.008
.012
.016
1 2 3 4 5 6 7 8 9 10 11 12
Response of LCPI to LM3
-.004
.000
.004
.008
.012
.016
1 2 3 4 5 6 7 8 9 10 11 12
Response of LCPI to INTERBANKR
-.02
.00
.02
.04
.06
.08
1 2 3 4 5 6 7 8 9 10 11 12
Response of LM3 to LM3
-.02
.00
.02
.04
.06
.08
1 2 3 4 5 6 7 8 9 10 11 12
Response of LM3 to INTERBANKR
-2
0
2
4
6
1 2 3 4 5 6 7 8 9 10 11 12
Response of INTERBANKR to LM3
-2
0
2
4
6
1 2 3 4 5 6 7 8 9 10 11 12
Response of INTERBANKR to INTERBANKR
-.04
-.02
.00
.02
.04
1 2 3 4 5 6 7 8 9 10 11 12
Response of LNEER to LM3
-.04
-.02
.00
.02
.04
1 2 3 4 5 6 7 8 9 10 11 12
Response of LNEER to INTERBANKR
Response to Cholesky One S.D. Innovations ± 2 S.E.
45
Table A10: Model 6-Variance Decomposition
Variance Decomposition of LIIP:
Period S.E. LIIP LCPI LM3 INTERBANK LNEER 1 0.030223 100.0000 0.000000 0.000000 0.000000 0.000000
2 0.032417 97.94881 0.537716 1.056736 0.279472 0.177264 4 0.033813 90.70843 2.460562 5.052804 0.694639 1.083567 8 0.036357 79.15002 6.145453 10.47808 1.151846 3.074601
12 0.038270 72.14648 8.706446 13.21633 1.451757 4.478986 Variance Decomposition of LCPI:
Period S.E. LIIP LCPI LM3 INTERBANK LNEER 1 0.016132 7.704930 92.29507 0.000000 0.000000 0.000000
2 0.024260 22.47478 75.22263 1.813450 0.451113 0.038030 4 0.035135 33.38500 59.32010 6.359173 0.674898 0.260834 8 0.045808 33.96631 50.98780 12.92985 0.932554 1.183490
12 0.051455 31.35929 48.12663 16.86560 1.224887 2.423598 Variance Decomposition of LM3:
Period S.E. LIIP LCPI LM3 INTERBANK
R LNEER 1 0.062962 0.807199 11.49054 87.70226 0.000000 0.000000
2 0.077631 2.424628 10.31911 86.90776 0.000652 0.347853 4 0.090101 5.817103 8.094749 83.96447 0.159197 1.964481 8 0.099997 5.897622 7.869938 79.57771 0.873017 5.781707
12 0.105853 5.315222 9.473446 75.70593 1.385673 8.119723 Variance Decomposition of INTERBANKR:
Period S.E. LIIP LCPI LM3 INTERBANK
R LNEER 1 3.878656 3.142882 0.509665 0.106607 96.24085 0.000000
2 4.277638 10.12765 1.750902 4.526980 82.91268 0.681792 4 4.463651 10.37427 4.725123 6.826352 76.28626 1.787998 8 4.660149 14.77753 6.260820 6.403762 70.02386 2.534030
12 4.711434 15.89258 6.600407 6.337924 68.51657 2.652520 Variance Decomposition of LNEER:
Period S.E. LIIP LCPI LM3 INTERBANK
R LNEER 1 0.059333 5.477425 1.406491 13.40050 1.694152 78.02143
2 0.078745 12.71528 0.938251 14.81570 3.034847 68.49593 4 0.095729 19.09332 0.734699 15.35068 3.770202 61.05109 8 0.105925 23.96220 1.756551 15.66269 3.619439 54.99912
12 0.109182 25.43968 2.947095 16.09229 3.422189 52.09875
46
Figure A6: Model 1 (Early Sub-period)-Impulse Responses
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LELECTRICITYG to LM3
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LELECTRICITYG to INTERBANKR
-.010
-.005
.000
.005
.010
.015
5 10 15 20 25 30 35
Response of LCPI to LM3
-.010
-.005
.000
.005
.010
.015
5 10 15 20 25 30 35
Response of LCPI to INTERBANKR
-.02
-.01
.00
.01
.02
.03
.04
5 10 15 20 25 30 35
Response of LM3 to LM3
-.02
-.01
.00
.01
.02
.03
.04
5 10 15 20 25 30 35
Response of LM3 to INTERBANKR
-4
0
4
8
12
5 10 15 20 25 30 35
Response of INTERBANKR to LM3
-4
0
4
8
12
5 10 15 20 25 30 35
Response of INTERBANKR to INTERBANKR
-.04
-.02
.00
.02
.04
5 10 15 20 25 30 35
Response of LNEER to LM3
-.04
-.02
.00
.02
.04
5 10 15 20 25 30 35
Response of LNEER to INTERBANKR
Response to Cholesky One S.D. Innovations ± 2 S.E.
47
Table A11: Model 1 (Early Sub-period)-Variance Decomposition
Variance Decomposition of LELECTRICITYG:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LNEER 1 0.067389 100.0000 0.000000 0.000000 0.000000 0.000000
6 0.091186 89.14950 0.596761 4.948038 3.842311 1.463394 12 0.091795 88.26864 0.617707 4.923171 4.216745 1.973734 24 0.091905 88.17544 0.646213 4.973148 4.215692 1.989511 36 0.091951 88.10836 0.681738 5.008754 4.212434 1.988716
Variance Decomposition of LCPI:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LNEER 1 0.012997 6.627666 93.37233 0.000000 0.000000 0.000000
6 0.036453 21.50396 58.93311 17.06117 0.499019 2.002742 12 0.047845 21.10092 48.69218 28.47831 0.441715 1.286875 24 0.061833 15.88347 44.24913 38.40068 0.614461 0.852251 36 0.072292 14.26663 42.45834 41.85690 0.667821 0.750313
Variance Decomposition of LM3:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LNEER 1 0.033033 0.016576 1.689973 98.29345 0.000000 0.000000
6 0.057927 1.229646 5.193407 84.49278 6.611557 2.472612 12 0.064688 2.372226 10.26998 75.88407 7.488587 3.985134 24 0.077017 8.232901 17.70496 64.65733 5.619300 3.785513 36 0.085492 8.737074 21.45855 61.90978 4.741235 3.153354
Variance Decomposition of INTERBANKR:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LNEER 1 8.256222 3.318481 0.011402 1.210666 95.45945 0.000000
6 10.31929 5.638300 4.189172 5.867300 82.46691 1.838316 12 10.60529 6.757267 4.610646 5.624558 78.40160 4.605929 24 10.80790 9.174853 4.885468 5.522236 75.52749 4.889957 36 10.84213 9.187662 5.093221 5.796370 75.06177 4.860975
Variance Decomposition of LNEER:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LNEER 1 0.033016 6.785622 0.051006 8.183236 0.432430 84.54771
6 0.103433 23.29695 4.905508 9.559549 6.725378 55.51261 12 0.120284 33.57374 4.407003 7.463301 7.554147 47.00181 24 0.125281 36.31227 5.247732 7.198647 7.283403 43.95795 36 0.126408 35.91944 5.821188 7.890113 7.182047 43.18721
48
Figure A7: Model 1 (Late Sub-period)-Impulse Responses
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LELECTRICITYG to LM3
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LELECTRICITYG to INTERBANKR
-.004
-.002
.000
.002
.004
.006
5 10 15 20 25 30 35
Response of LCPI to LM3
-.004
-.002
.000
.002
.004
.006
5 10 15 20 25 30 35
Response of LCPI to INTERBANKR
-.01
.00
.01
.02
.03
.04
.05
5 10 15 20 25 30 35
Response of LM3 to LM3
-.01
.00
.01
.02
.03
.04
.05
5 10 15 20 25 30 35
Response of LM3 to INTERBANKR
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
5 10 15 20 25 30 35
Response of INTERBANKR to LM3
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
5 10 15 20 25 30 35
Response of INTERBANKR to INTERBANKR
-.03
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LNEER to LM3
-.03
-.02
-.01
.00
.01
.02
.03
5 10 15 20 25 30 35
Response of LNEER to INTERBANKR
Response to Cholesky One S.D. Innovations ± 2 S.E.
49
Table A12: Model 1 (Late Sub-period)-Variance Decomposition
Variance Decomposition of LELECTRICITYG:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LNEER 1 0.053413 100.0000 0.000000 0.000000 0.000000 0.000000
6 0.064722 80.78515 3.166141 11.52032 2.206490 2.321902 12 0.067719 74.88460 6.204323 11.99993 4.087288 2.823854 24 0.068379 73.49546 6.902888 12.51177 4.176754 2.913130 36 0.068831 72.55685 7.257348 12.77234 4.197256 3.216205
Variance Decomposition of LCPI:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LNEER 1 0.004626 0.695063 99.30494 0.000000 0.000000 0.000000
6 0.012630 1.065435 68.29798 25.18740 3.187178 2.262011 12 0.016775 2.170026 54.70273 33.89597 2.122552 7.108717 24 0.022182 2.109474 45.19374 33.09918 4.087012 15.51059 36 0.025943 2.031403 39.61957 32.26722 6.704868 19.37694
Variance Decomposition of LM3:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LNEER 1 0.037058 0.002104 0.106649 99.89125 0.000000 0.000000
6 0.057507 8.861625 1.269961 81.06279 6.249064 2.556561 12 0.066456 7.731988 6.925594 66.51803 8.759854 10.06454 24 0.079263 5.887722 11.73096 55.24283 10.82226 16.31623 36 0.088019 5.106101 13.48256 50.17374 12.13565 19.10194
Variance Decomposition of INTERBANKR:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK
R LNEER 1 1.779050 3.332834 0.042419 0.658032 95.96672 0.000000
6 2.826802 1.867688 1.761539 11.43881 83.81961 1.112356 12 3.119280 2.358400 2.281098 11.30331 80.99366 3.063525 24 3.247627 2.215931 2.123772 10.87368 79.71311 5.073508 36 3.282186 2.192596 2.162924 10.97871 79.02880 5.636974
Variance Decomposition of LNEER:
Period S.E. LELECTRICI
TYG LCPI LM3 INTERBANK LNEER 1 0.037790 0.304184 3.609009 20.57147 5.458392 70.05694
6 0.065027 1.450357 2.928089 20.13947 23.02868 52.45341 12 0.070384 1.959881 4.119569 17.82619 28.93300 47.16136 24 0.072679 1.843867 5.851848 16.94342 30.96937 44.39149 36 0.073100 1.840102 6.376911 17.06958 30.73555 43.97786