FINANCIAL LIBERALISATION AND ECONOMIC GROWTH:

A VECTOR AUTOREGRESSION APPROACH

Okunoye Ismaila

University of Ibadan, Ibadan, Nigeria

Tel: +2348107504642; E-mail; adeleye013@gmail.com;

ABSTRACT

The paper models banking sector liberalisation variables between financial liberalisation and economic growth in Nigeria for the period of 1981 to 2012, based on time series econometrics using granger causality through Vector AutoRegression method. It examines the extent of banking sector liberalisation on growth of the economy using growth in credit to private sector to GDP, growth in demand deposit to narrow money, growth in net domestic credit to GDP, growth in net broad money stock to GDP, and growth in currency in circulation to broad money stock. The finding from the paper indicates that there is no stable long run relationship between the banking sector liberalisation and economic growth in Nigeria. The causality runs between currency in circulation and economic growth, and domestic credit and economic growth. The results suggest the financial sector development can enhance economic growth and development of the country if the growth in currency in circulation, positively influenced by demand deposit and credit to the private sector, is fully monitored to improve investment in real sectors of the domestic economy. The findings suggest that Central Bank of Nigeria with the collaboration of Bankers’ Committee need to adopt more stringent penalties related to unsafe insider and related party lending that does not enhance investment in the real sectors of the economy which is source drivers of growth.

Keywords: Financial liberalisation, time series econometrics, granger causality, Vector Autoregression

JEL Classification

1. Introduction

Despite laudable achievements and certain challenges that have confronted the Nigerian financial

system, the Nigerian economy still witness substantial growth in the separate and co-dependent

components of its financial intermediaries, financial market and financial infrastructural component.

Financial liberalisation is not a contemporary phenomenon. In particular, financial liberalisation may

focus on policies such as banking sector liberalisation, stock market liberalisation, and capital account

liberalisation. For decades, Western countries and companies were operating in a free economy. Thus, it

is difficult to identify the beginnings of financial liberalisation, on which is based the economy

liberalisation. In the late 1970s, the phenomenon of deregulation in the U.S. appears to be the object of

widening freedom to innovate and take in a system of pure competition, by reducing or scrapping rules

authoritarian, which tended to strictly regulate the role and freedom of financial intermediaries action to

impose limits framework lending operations by their nature and duration. Financial liberalisation

includes several elements, as it reflects a variety of restrictions that were imposed. Generally like Nigeria,

financial system in other developing countries witnessed a substantial growth after adopting the mix of

economic measures under the IMF/World Bank supported Structural Adjustment Programme prescribed

as comprehensive reform measures designed to achieve both internal and external balance with a

minimum cost to the economy. A summary of Nigeria’s macroeconomic indicators has revealed a serious

decline in all areas of variable until the introduction of the SAP of 1986. The real effective exchange rate,

for instance, fluctuated between 326 and 652.3 between the period of 1980 and 1986 but went down

drastically to 105 by 1987 but then rose to 699 in 1992. Also, the real gross domestic product growth

rate hovers around -0.32; until 1986 when it started experiencing a positive growth rate.

Due to the high dependence of exports of crude oil as well as foreign capital inflows that compounded

the impact of the external shock arising from the crisis, coupled with the collapse in the international

price of oil, led to severe decline in foreign exchange receipts and volatility and substantial depreciation

of the naira exchange rate, and consequently, government revenue contraction. This confirms the high

level of fragility of Nigeria capital market where the greatest impact was felt. Through the excess liquidity

that found its way into the stock market and banking system which put the banking sector at serious risk

when the global financial crisis of 2008 set in. The 2007/08 global financial and economic crisis which

triggered distressed assets, high bank insolvency and loss of market trust in developed economies, and

threatened large-scale private sector default in emerging economies (IMF, 2009), have led to renewed

interest on the role of financial liberalization in economic development. Many analysts traced the cause

of the crisis to the fallout of financial liberalization and the regime of easy credit in the United States.

This have an indirect effect on Nigeria financial institution and as well not surprising therefore that given

the gravity of the crisis, the demand for government loan bonds comes mainly from financial institution

such as commercial banks and saving-type institutions. It is against this backdrop that a well functioning

banking sector is a precondition for the efficient allocation of resources and the exploitation of an

economy’s growth potential, through other elements of financial liberalisation, the economic literature is

less consensual on how and to what extent banking sector liberalisation has sustain economic growth.

This current study seek answers to, (i) what is the nature of relationship between banking sector

liberalisation and economic growth in Nigeria as well investigate the identification of each channel

through which financial sector affect economic growth (ii) to evaluate the impact of banking sector

liberalization variables on sustainability of economic growth in Nigeria. This study attempts to find

answers to these questions to show the new evidence from Nigeria using an integrated method. This

study will be of utmost importance in providing policy recommendation to the various stakeholders in

Nigeria towards the impact of banking sector liberalisation policies in sustaining economic growth, and in

emerging nations in taking adequate measures in their economy for rapid growth using the findings from

the Nigerian experience as a benchmark to conclude on the efficacy of banking sector liberalisation in

developing economies and make recommendations.

The Nigerian financial system has been recognized as major component of the country’s growth

and development agenda. The government had since the 1970s been characterized by high quasi total

regulation as the government owned majority of the shares of most of the banks existing at the time.

However, by the early 1980s, unfavourable and deteriorating economic conditions implied that the

economy needed some form of structural adjustment. The banking system became weak and fragile and

was thus unable to perform its primary function of financial intermediation. In 1986, the liberalization of

the banking industry was a major component of the Structural Adjustment Programme (SAP) put in place

at that time to drive the economy from austerity to prosperity. In 2004, the banking sector

reform/consolidation exercise in the banking industry took a leading role in the National Economic

Empowerment and Development Strategy (NEEDS), which was in place at that time to drive the

economic agenda of the government. This required all Deposit Money Banks (DMBs) to shore up their

capital base from the hitherto N2.5billion to N25billion naira. This regulation reduced the number of

banks from 89 to 25 commercial banks by the end of 2004.

In 2009, as part of the broad economic measures to respond to the adverse effects of the global financial

and economic crises, the Central Bank of Nigeria in conjunction with the fiscal authorities engineered

measures to avert a collapse of the financial system with a view to maintaining economic growth. The

essence of emphasis on the development of the Nigerian financial sector is in the theory of financial

repression which posits that efficient utilization of resources through a highly organized,

developed and liberal financial system enhances economic growth (McKinnon, 1973; Shaw, 1973,

Odeniran and Udeaja, 2010).

Following this introduction section, the remaining sections of the paper are divided into five. Section 2

describes overview of Nigeria’s Financial Structure. Section 3 describes review of related literature as

well framework of analysis. Section 4 presents the econometric methodology implemented in the study.

Section 5 discusses the empirical results while Section 6 concludes by examine the policy implication of

the study.

2. Overview of Nigeria’s Financial Structure

A financial system is a conglomerate of institutions, markets, instruments and operators that

interacts to provide such financial services as resource mobilization and allocation, financial

intermediation and facilitation of foreign exchange transactions. The Nigerian financial system can be

categorized into formal (money and capital market institutions) and informal (local money lenders, the

thrifts and savings associations and so on) groups. In general the Nigerian financial system comprises the

regulatory/supervisory authorities, banks and non-bank financial institutions. By the end of 2006, the

system comprised Central Bank of Nigeria (CBN), the Nigeria Deposit Insurance Corporation (NDIC), the

Securities and Exchange Commission (SEC), the Federal Ministry of Finance, Federal Mortgage Bank of

Nigeria, the National Insurance Commission (NAICOM), the Financial Services Regulatory Coordinating

Committee (FSRCC), the National Pension Commission (NPC), 25 Deposit Money Banks, 750 Community

(now Microfinance) Banks, 112 Finance Companies, 322 Bureau-de-Change, one Stock Exchange with

branch offices, one Commodity Exchange at Abuja, 91 Primary Mortgage Institutions, 5 Microfinance

Banks and 581 Registered Insurance Brokers (CBN, 2006).

The Banks and Non-Bank Financial Institutions comprises the money market with the following

institutions: Deposit Money Banks (DMBs), the Discount Houses (DHs), the Development Finance

Institutions (DFIs)/Specialized Banks which are further subdivided into Urban Development Bank (UDB),

the Nigerian Export-Import Bank (NEXIM) and the microfinance banks. Besides the money market, the

capital market comprises the following institutions: the Nigerian Stock Exchange (NSE), the Abuja Stock

(now Commodity) Exchange, the Primary Market, the Secondary Market and the Unit Trust Scheme.

Other non-bank financial institutions and funds include finance companies, insurance companies, bureau

de change, primary mortgage institutions (PMIs), the Nigerian Social Insurance Trust Fund (NSITF) and so

on.

In 2004, the current reforms which began with the consolidation programme were necessitated

by the need to strengthen the banks. The policy thrust at inception, was to grow the banks and position

them to play pivotal roles in driving development across the sectors of the economy. As a result, banks

were consolidated through mergers and acquisitions, raising the capital base from N2 billion to a

minimum of N25 billion, which reduced the number of banks from 89 to 25 in 2005, and later to 24.

Although, the consolidation exercise made the banks to be highly capitalised, people were surprised to

hear the pronouncement of the newly appointed Governor of the Central Bank of Nigeria on 21 June,

2009, that all were not well with the Nigerian banks. The audit test by the Central bank of Nigeria of the

24 banks in August, 2009, revealed that only 14 banks were found to have adequate capital and liquidity

to support the level of their current operations and future growth, while 2 banks were asked to re-

capitalise before 30 June, 2010, and 8 banks were adjudged to be in grave situations. The Central Bank of

Nigeria, citing the provisions of the Banks and Other Financial Institutions Act 2004 sacked the Executive

Management of the 8 banks. The criteria employed for the special examination in all the banks were:

Liquidity, capital adequacy, and corporate governance. The apex bank subsequently injected N620 billion

(about US$4.03 billion) in the banks as liquidity support and long term loans. The action of CBN implies

that the banks have not complied with the laid down principles of good banking of ensuring that the

banking system is safe, sound and stable.

Similarly, the importance of Microfinance in a growing economy cannot be over-emphasized,

given its potential in addressing the challenges of excluding a large population from full participation in

economic activities. As at December 2011 there were 24 deposit money banks with 5,789 branches and

816 microfinance banks bringing the total bank branches to 6,605. The ratio of bank branch to total

population is 24,224 persons, indicating a high level of financial exclusion. Under the new Universal

model adopted in 2001, licensed banks will be authorized to carry the following types of business:

Commercial banking (with regional, national or international authorization); Merchant (investment)

banking; Specialized banking (microfinance, mortgage, non-interest banking (regional and national); and

Development finance institutions. The introduction of the non-interest banking in Nigeria is expected to

herald the entry of new markets and institutional players thus deepening the nation’s financial markets

and further the quest for financial inclusion. In fact, the first fully licensed non-interest bank in the

country (Jaiz Bank Plc.) started business on Friday, January 6, 2012.

3. Review of Related Literature

Generally, from the reviewed literature and empirical studies on the finance-growth nexus there

are four documented views; supply leading, demand following, mutual impact of finance and growth and

those that the role of finance in promoting growth is overemphasized. And several empirical studies

carried out using different estimation method on the finance-growth nexus for countries and it is

observed that while some studies have employed a single indicator of financial development, others

have used two or more indicators separately to analyse the underlying relationship. The evidence from

studies on the relationship between financial development and economic growth suggests enormous

dissimilarity across countries, regions, financial factors and direction of causality. These studies can be

broadly classified based on the econometric procedures employed which could be single-period, cross-

section analysis, time series, or panel data econometrics.

Empirical studies on this subject burgeoned in the 1990s, following the prominent work of King

and Levine (1993a). In a study of 80 countries over the period 1960-89 in which they controlled for other

factors that affect long-run growth, their results showed that initial level of financial development is a

good predictor of the subsequent rates of economic growth. Other studies by Benhabib and Spiegel

(2000), Levine, Loayza and Beck (2000) and Rioja and Valev (2004) point to the same conclusion that

financial development has a positive impact on economic growth, however, these broad comparative

analyses conducted at the aggregate level are unable to account for the complexity of the financial

environment and specific institutional context of each individual country (Ang, 2007). Darrat (1999),

contributing to the role of financial deepening on economic growth examined three Middle-Eastern

countries of Saudi Arabia, Turkey, and the United Arab Emirates using multivariate Granger causality

tests within an error-correction framework. They tried to determine the causal link between financial

deepening and economic growth in order to discriminate between several alternative theoretical

hypotheses. The results generally support the view that financial deepening is a necessary causal factor

for economic growth, although the strength of the evidence varied across countries and across the

proxies used to measure financial deepening. The causal relationships were also predominately long-

term in nature hence their recommendations that government policies aimed at promoting financial

deepening in those countries must be persistent and sustainable in order to foster economic

development.

Bhatia and Khatkhate (1975) used correlation graphs to examine the relationship between

economic growth and financial intermediation for eleven African countries. Financial intermediation is

measured by the ratio of currency, demand deposits, and time and savings deposits to GDP. The authors

find no definite relationship between growth and financial intermediation for the countries individually.

Splitting the financial intermediation measure into two – the ratio of money to GDP and the ratio of

quasi-money to GDP – still do not reveal any definite relationship between growth and financial

intermediation. Ogun (1986) used cross-section analysis to estimate the correlation between financial

deepening and economic growth by using data for 20 countries in Africa from 1969 – 1983. The degree

of financial intermediation is measured using the ratio of monetary liabilities (M1, M2 and M3) to GDP.

For the full sample, all the monetary liabilities are negative and only the ratio of M3 to GDP is statistically

significant.

Oshikoya (1992) also used time series econometrics to see how interest rate liberalisation has

affected economic growth in Kenya. The author used data from 1970 to 1989 and the results showed a

negative and significant coefficient for the 1970-1979 period, but was positive and significant for the

1980-1989 period; thus offering no robust result of the effect of interest rate liberalisation on growth.

Choe and Moosa (1999) further examined the relationship between financial development and economic

growth for Korea. The study focused on relative development of financial intermediaries and capital

markets. Causality test shows that financial development in general leads to economic growth. King and

Levine (1999) present evidence that support Schumpeter’s view that financial development leads to

economic growth. The study covers the period 1960 to 1980 for 80 countries. It was a cross-section

regression of real GDP per capita on measures of financial development and other variables such as

initial GDP and the ratio of investment to GDP. The results confirmed that financial development causes

economic growth. Luintel and Khan (1999) examined the long-run relationship between financial

development and economic growth using multivariate vector autoregression for 10 countries. The

examination revealed that there is a bi-directional causality between financial development and

economic growth for all sampled countries.

Besides, Ghali (1999) investigated empirically whether financial development leads to economic

growth in Tunisia. The investigation was also done using the vector autoregression technique. The

empirical results suggest that there is a stable long-run relationship between financial development and

economic growth. The causality runs from financial development to economic growth. Calderon and Liu

(2003) employ a Geweke decomposition test on pooled data of 109 countries (developed and

developing) for the period 1960 to 1964 to examine the causal relationship between financial

development and economic growth. The study confirms that generally, financial development causes

economic growth. There is a bi-directional causality between financial development and economic

growth. Financial development contributes more to economic growth in developing countries than in

developed countries.

Allen and Ndikumana (2000) used the ratio of liquid liabilities, ratio of banks’ private sector

credit, ratio of banks’ total credit, and an index to include all three measures as proxies for financial

intermediation. The authors find that only the ratio of liquid liabilities is positive and significant, and

even this variable is insignificant in the fixed effects estimation and when annual data are used. The

other financial intermediation variables take on different signs but are all insignificant. Furthermore, we

cannot ascertain the effect of financial liberalisation policies on economic growth from these studies

because none of them explicitly measures the different stages involved in financial liberalisation.

Although some of them (Seck and El Nil, 1993; Oshikoya, 1992, Fowowe 2008) try to model liberalisation

by including the real rate of interest as an explanatory variable, liberalisation involves more than just

interest rate liberalisation. It includes an array of measures such as directed credit abolition, bank

denationalisation, and the removal of restrictions of entry into banking. Measuring financial liberalisation

through the real interest rate thus negates the impact of the other liberalisation policy moves and

implies that estimates derived suffer from omitted variable bias (Gibson and Tsakalatos, 1994;

Demetriades and Luintel, 1996). Existing empirical studies (Kasehende and Atingi-Ego, 2003; Suleiman

L.A, Oke M.O, Azeez B.A, 2012) have not adequately identified which element of financial liberalisation

sustained the gradual institutional changes that financial liberalisation measured.

Aziakpono (2004) used the ratio of liquid liabilities and the ratio of banks’ private credit as

measures of financial intermediation and found mixed results. Odhiambo (2004) also investigated the

direction of causality between financial development and economic growth for South Africa using a

vector error correction model. The investigation revealed that the supply-leading hypothesis is rejected

for South Africa. There is a strong evidence of demand-leading hypothesis for South Africa. This implies

that the causality runs from economic growth to financial development and shows that economic growth

drives financial development in South Africa. Odhiambo (2005) applied the same methodology to

Tanzania and the results show that there is bi-directional causality between financial development and

economic growth, although the supply-leading hypothesis dominates. Abu-Bader and Abu-Qarn (2005) in

an attempt to examine the relationship between financial development and economic growth in Egypt,

analyzed time series annual data from 1960 to 2001 using VAR methodology on four variables namely:

Gross Domestic Product to measure economic growth and ratio of money stock to nominal

GDP, ratio of bank credit to the private sector to nominal GDP, ratio of credit issued to non -financial

private firms to total domestic credit, representing proxies for financial development. Their findings

show that the rise in private investment was facilitated by the financial liberalization in 1990 which led to

the rebound in economic performance of Egypt in the 1990s. Their results infer that there is a direct

linkage between financial development and financial liberalization. Seck and El Nil (1993) and Charlier

and Oguie (2002) find a significantly positive relationship between economic growth and the real interest

rate. Another study on financial development and economic growth was conducted by Apergis N.,

Filippidi, I. and Economidou, C. (2007). It employed panel data integration and co-integration for a

dynamic heterogeneous panel of 15 OECD countries over the period 1975 to 2000. The findings reveal

that there is a long-run relationship between financial deepening and economic growth. The causality

between financial development and economic growth is bi-directional.

4. Methodology

Description of Variables and Data

The study employed time series econometric analysis. The study employed annual data on selected

variables from 1981-2012. The study used multiple regression models for empirical analysis; Ordinary

Least Square, Augmented Dickey Fuller and Phillip Perron Test for Unit Root test and further with

Granger Causality tests in a VAR framework. As in the empirical literature, real GDP per capita is used to

measure real growth rates. However, a limitation of studies on the financial sector is that there is no

single measure of financial sector development, therefore, instead of a single proxy; five measures

are employed in this study in order to improve the robustness of the results.

The first measure is M2-to-GDP (MCY) ratio otherwise known as measure of financial deepening.

The M2 (minus currency in circulation) to GDP, the ratio measures the degree of monetization in the

economy as well as the depth of the financial sector while it also shows an expansion of

payment and saving functions. The second measure used in the study is the ratio of domestic

credit to GDP (DCY). This represents the domestic assets of the financial sector as well as firms’ use of

credit in addition to internal funds and it is expected to change in response to change in real interest

rates. The third ratio employed in this study is demand deposit to narrow money, M1 (DDM1), which

reflects the complexity, or sophistication of the financial market (primarily banks). An increase in this

ratio implies a higher degree of diversification of financial institutions and a greater availability or use of

non-currency balances (bank deposits) in the transaction process. The fourth ratio employed in this study

is currency in circulation to M2, CCM2, which reflects the more extensive use of currency than an

increase in the volume of bank deposit. The last measure is the ratio of private sector credit to

GDP(CPY). The basis for this indicator is that commercial financial intermediaries are able to

identify profitable investments, monitor managers, facilitate risk management, and mobilize savings.

The sources of all the data used in the study are; Central Bank of Nigeria Statistical Bulletin 2012 and

National Bureau of Statistics 2012. The general form for a regression analysis is given in the form below;

Y= β0 + β1X1 + β2X2 + β3X3 + … + βnXn + μ ------------- (3.1 )

Where; Y = Dependent variable

β0 = Equation constant

β1, …,βn = Coefficients of explanatory variables

X1,…, Xn = Independent or explanatory variables

μ = Error term

5. Empirical Results

Result from Ordinary Least Square (OLS)

Y= β0 + CPYβ1+ CCMβ2+ DDMβ3 + DCYβ4 + M2Yβ5 + μ ----------- (3.2)

Dependent Variable: GDPGRMethod: Least Squares

Sample (adjusted): 1982 2012

Included observations: 31 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

DDM1 -0.883225 1.969585 -0.448432 0.6577

DCY -0.382476 0.893770 -0.427936 0.6724

CPY 1.128879 1.648992 0.684587 0.4999

CCM2 -0.779471 2.160052 -0.360857 0.7212

M2Y -1.402472 2.069534 -0.677675 0.5042

C 1.177338 1.639049 0.718305 0.4792

R-squared 0.137265 Mean dependent var 0.232514

Adjusted R-squared -0.035282 S.D. dependent var 0.221630

S.E. of regression 0.225506 Akaike info criterion 0.031049

Sum squared resid 1.271328 Schwarz criterion 0.308594

Log likelihood 5.518748 Hannan-Quinn criter. 0.121522

F-statistic 0.795523 Durbin-Watson stat 1.711038

Prob(F-statistic) 0.563164

Y= 1.177 + 1.129CPY - 0.779CCM – 0.883DDM - 0.382DCY – 1.402M2Y + μ ------- (3.3)

From the result/analysis, CPY also called credit to private sector/GDP, had a positive non-significant

impact on gross domestic product growth rate in Nigeria (t=0.684, coefficient of CPY= 1.129). This ratio

indicates that the financial sector impact positively as expected to induce economic growth. CCM, also

called Currency in circulation/Broad Money, had a negative non-significant impact on gross domestic

product growth rate in Nigeria (t=0.360, coefficient of CCM = -0.779). This ratio signifies that more

extensive use of currency did not impact positively on economic growth in Nigeria. DDM, also money

stock diversification, had a negative non-significant impact on the gross domestic product growth rate in

Nigeria (t-0.448, coefficient of DDM = -0.883). This ratio implies that the Nigerian economy is not

sophisticated enough to increase the degree of diversification of financial institutions which will provide

greater use of non-currency balances in its arrangement. DCY, also called domestic credit/GDP, had a

negative non-significant impact on gross domestic product growth rate in Nigeria (t=0.427, coefficient of

DCY=-0.382). This implies the extent to which societies and firms’ credit allocated by the financial

institution, to induce domestic investment had not shown expected impact on the economic growth.

However, broad money (excluding currency in circulation) to GDP, also called M2Y, had a negative non-

significant impact on economic growth (t=0.677, coefficient of M2Y= -1.402). This ratio implies that an

expansion of the financial sector had not impacted positively on the economy. The coefficient of the

constant parameter (B0) is 1.177. This implies that if all the explanatory variables are constant, GDP

which is the explained variable will increase by 1.18 units.

It is almost a convention in time series analyisis, after the Ordinary Least Square result to verify the order

of integration for each series to avoid the problem of spurious regression. The enquiry into stationary

property of each variable is conducted using Augmented Dickey Fuller (ADF) and Phillip-Perron (PP) test

procedures. The Phillip-Perron test method which computes a residual variance that is robust to auto-

correlation is employed as alternative to the ADF. The decision rule is that ADF test statistics must be

greater than Mckinnon critical value at 5% ( or 10%) and at absolute term, i.e ignoring the negative value

of both the ADF test statistics and Mckinnon critical value, before the variable is adjudged to be

stationary, otherwise we accept the null hypothesis (H0) that data is non-stationary and reject the

alternate hypothesis (H1) that data is stationary. The results will be presented as follows:

Results from Unit Root Test

Augmented Dickey-Fuller and Phillip Perron Unit Root Test TableVariables Augmented Dickey-Fuller

(ADF)TestLevel First Diff

Phillip Perron (PP) Test Level First Diff

Decision Rule for ADF

Decision Rule for PP

CCM2 -2.31 -1.44 -0.49 -3.34 I(2) I (1)CPY -1.69 -5.11 -0.78 -3.98 I (1) I (1)DCY -2.04 -4.84 -2.10 -5.69 I (1) I (1)DDM1 -0.38 -4.14 -0.19 -4.16 I (1) I (1)M2Y -0.64 -4.32 -0.76 -4.19 I (1) I (1)Critical 5% and 10%

Values are -2.69 and -3.69

Source: Author’s Computation

With the results of the above unit root tests suggesting that all the variables are stationary of the order

I(1) using PP test, we move a step further to employ the Johansen procedures to test for co-integration

among the variables. The Johansen methodology is a generalization of the Dickey-Fuller test. Two

likelihood ratio test (trace and eigen value) were used to test the hypothesis regarding the number of co-

integrating vectors. The result of the co-integration test will be presented as follows;

Results from Co-Integration

Co-integration Test Table (Trace and Maximum Eigenvalue)

Eigen value Likelihood ratio

5% critical value

Max –Eigen value

5% critical value Hypothesized no of CE(s)

0.816 109.97 95.75 50.86 40.07 Co-integ

0.582 59.11 69.82 26.18 33.88 Nt co-integ

0.439 32.92 47.85 17.36 27.58 Nt co-integ

0.251 15.55 29.79 8.69 21.13 Nt co-integ

0.205 6.87 15.49 6.87 14.26 Nt co-integ

1.92E-06 5.75E-05 3.84 5.75E-05 3.84 Nt co-integ

Source: Author’s computation

Given the Johansen co-integration procedures, the trace statistics (likelihood ratio) show that there is

one co-integrating vector among the five proxies for financial liberalization variables at the 5% level. The

maximum eigen-value shows that there is one co-integrating vector at the 5% level; this is in consonance

when each of the variables is co-integrated with GDP individually.

The economic interpretation of these statistical tests revealed that there is one co-integrating vector,

DCY and GDP, and this is only significant at the 5% level which is indicative of a weaker association. In

this regard, it is infer that there was no stable long run relationship between financial liberalization and

economic growth in Nigeria. The study also employed Vector AutoRegression (VAR) method to find out

the adjustment in the variables when affected by shocks and as well to capture the co-movement of the

variables that cannot be detected in the model. VAR model advocates using of granger causality test,

variance decomposition and impulse response functions. The result of the causality test will be

presented as follows;

Results from Granger Causality Test

Pairwise Granger Causality TestsSample: 1981-2012Lags:2

Null Hypothesis: Obs F-Statistic Prob.

CCM2 does not Granger Cause GDPGR 30 7.62731 0.0026 GDPGR does not Granger Cause CCM2 1.08504 0.3533

CPY does not Granger Cause GDPGR 30 0.12528 0.8828 GDPGR does not Granger Cause CPY 0.21553 0.8076

DCY does not Granger Cause GDPGR 30 2.49687 0.1027 GDPGR does not Granger Cause DCY 0.10769 0.8983

DDM1 does not Granger Cause GDPGR 30 7.98961 0.0021 GDPGR does not Granger Cause DDM1 0.50209 0.6112

M2Y does not Granger Cause GDPGR 30 0.12317 0.8846 GDPGR does not Granger Cause M2Y 0.25924 0.7737

CPY does not Granger Cause CCM2 30 0.74354 0.4857 CCM2 does not Granger Cause CPY 2.66632 0.0892

DCY does not Granger Cause CCM2 30 0.01360 0.9865 CCM2 does not Granger Cause DCY 2.34791 0.1163

DDM1 does not Granger Cause CCM2 30 2.18626 0.1333 CCM2 does not Granger Cause DDM1 1.02264 0.3742

M2Y does not Granger Cause CCM2 30 0.96529 0.3946 CCM2 does not Granger Cause M2Y 5.07753 0.0141

DCY does not Granger Cause CPY 30 3.24866 0.0557 CPY does not Granger Cause DCY 1.34576 0.2786

DDM1 does not Granger Cause CPY 30 6.14906 0.0067 CPY does not Granger Cause DDM1 0.42277 0.6598

M2Y does not Granger Cause CPY 30 0.95333 0.3990 CPY does not Granger Cause M2Y 0.06632 0.9360

DDM1 does not Granger Cause DCY 30 2.35480 0.1156 DCY does not Granger Cause DDM1 0.23177 0.7948

M2Y does not Granger Cause DCY 30 1.75378 0.1938 DCY does not Granger Cause M2Y 8.11743 0.0019

M2Y does not Granger Cause DDM1 30 0.33620 0.7177 DDM1 does not Granger Cause M2Y 11.3358 0.0003

From the table, the results of the selected variables of financial liberalization and economic growth are

tested for bi-directional relationship, as well short run relationship using granger causality test. The test

showed CPY, CCM2, DDM1, M2Y, and DCY as financial liberalization variables and gross domestic product

as economic growth. The results suggest that CPY, CCM2 and DDM1 granger-cause gross domestic

product. In addition, gross domestic product granger-cause CCM2.

More so, there is a bi-directional causality among CPY to CCM2, DDM1 to CCM2, M2Y to CCM2, DCY to

CPY, DDM1 to CPY, M2Y to DCY which suggest that variables granger-cause each other.

The result on domestic credit is in tandem with findings in some developing economies. In China, for

instance, Jean-Claude (2006) shows evidence of causality from domestic credit to economic growth

which was predicated on the fact that large share of the state budget and direct credit in China

constitute some of the official development tools used by the Chinese authorities. The result revealed

that the various measures of financial sector development have impact on economic growth contrary to

most of the earlier studies on Nigeria. In view of this finding, the development of the financial sector is

very critical to overall economic growth. Nevertheless, the bi-directional causality on domestic credit and

credit to private sector, demand deposit to credit to private sector implies that both demand-led and

supply-led hypotheses hold in Nigeria, supporting the finding of Agu and Chukwu (2008).

Results from Variance Decomposition (see Appendix 1)

The results of variance decomposition of the model over a 10-period horizon are presented in the table

above. The variance decomposition apportions the total fluctuations in a particular variable to the

constituent innovations in the system. The results show that the variables are largely driven by

themselves, except DDM1, M2Y and DCY. The contribution of CCM2 to the variation in output growth

became significant from the second period when it reaches 31 per cent. The CPY contributes about 2

percent to the innovations in output growth by second period which increases to 11 percent in the tenth

period. The contribution of other variables is insignificant except M2Y which became significant with 7

percent in the sixth to the tenth period. Thus, the principal drivers of output growth are itself and CCM2.

The variance of CCM2 is driven primarily by itself in the first period, contributing 98.7 percent to the total

variations. The contribution of CPY and DCY became noticeable in the fourth and eighth period

respectively with 19.4 percent and 10.5 percent. Thus, the principal driver of CCM2 is itself with 51 per

cent in the ninth period.

The variance of CPY is driven primarily by itself and other variable, CCM2 and GDP, became noticeable

and significant in the fifth period till it reaches tenth period.

The variance of DCY is driven primarily by output growth, and other variables, GDP and CPY, became

noticeable and significant in the fifth period to the tenth period. Thus, the principal drivers of DCY are

itself, CPY and GDP.

The variance of DDM1 is driven primarily by CCM2 and other variables, DDM1,CPY and GDPGR became

significant in the first period, second period and fourth period respectively. Thus, by tenth period the

principal drivers of DDM1 are CCM2, GDP, CPY and DCY respectively.

The variance of M2Y is driven primarily by CPY, GDP and itself. By second period, CCM2 became

significant with 30.4 per cent, rises to 32.9 per cent by the end of the tenth period. The contribution of

CCM2, GDP and CPY to the variation in the M2Y became significant in the second period, with 30

percent,14per cent and 27 per cent till it reaches the end of tenth period.

In summary, the variance decomposition shows that the significant variation for each variable is due to

its own variation, except DCY, DDM1 and M2Y. The result of variance decomposition analysis confirmed

that CCM2 contributed in the total variation of itself, GDP and other variables significantly suggesting

that a specific shock in CCM2 have a significant impact in forecasting other variable and output growth.

Results from Impulse Response Function (see appendix 2)

From the table, the result of the impulse response function which traced the responses of the system

variables to one standard deviation shocks to the system innovations spanning over the five (5) periods.

The result showed that each variable responded significantly to its own one standard deviation shock.

Meanwhile, the results revealed output growth responded to shocks in CCM2, DCY and DDM1. This

means that a one standard deviation to CCM2, DCY and DDM1 would commence a moderate shock in

output growth from the second period and that it would rise till the end of the tenth period. This

denotes that rise in output growth is driven by CCM2, DCY and DDM1 which necessitates the increase in

currency in circulation i.e. growth in working capital, from the domestic savers’ deposits.

Therefore, it could be observed from the impulse response function that rise in output growth is

primarily driven by CCM2, DCY and DDM1 even though not significant. This has implication for

policymakers and the government to direct financial intermediation to real sectors of the economy

which is source drivers of growth.

6. Summary, Policy Implication and Conclusion

Financial markets have an important role in economics. Development in financial system means

improving functions, policies and evaluation which takes part in the financial system. Development in

financial sector can be achieved through financial liberalization, more generally, to develop and promote

the role of financial markets in financing the economy as well as strengthening the functioning of

financial system.

The results of testing the banking sector liberalization impact on sustainability of economic growth in

Nigeria. The findings show that there was a positive impact of credit to the private sector while other

banking sector liberalization variables that signify the greater use of non-currency balances/bank deposit

(DDM1), societies’ and firms’ credit allocated by the financial institution (DCY), extensive use of currency

(CCM2), and broad money velocity (M2Y) had a negative impact on the gross domestic product growth

rate in Nigeria, even though insignificant.

In view of this, the study of financial liberalization indicators on sustainability of economic growth in

Nigeria for the period of 1981 – 2012 employed granger causality through Vector Auto Regression

method, found that there is no stable long run relationship between the banking sector liberalization and

economic growth in Nigeria. Unlike most of the earlier studies, the major empirical results show that

financial liberalization does not have any influence on Nigeria’s economic growth. The VAR results

indicate that changes in currency in circulation and domestic credit impact on economic growth while

output also influences currency in circulation and domestic credit, that is, there is bi-directional causality

between domestic credit and economic growth and currency in circulation and economic growth. In view

of this findings emanating from this study while desired impacts are yet to be achieved, government

policies direction should focus on how currency in circulation, which is granger-cause by demand

deposit, domestic credit and credit to private sector, will be monitored and directed to the real sector to

improve the gross domestic product growth rate of the economy.

The long run responses of the system variables to one standard deviation shocks show that a one

standard deviation shock to currency in circulation, domestic credit and demand deposit would leads to

a significant positive response in output from the second quarter and the increases would be sustained

up to the tenth quarter horizon. The fact that the growth in the currency in circulation positively

influences demand deposit has major implications.

Finally, to fully realize the growth potentials of the Nigerian economy it is necessary to remove all

obstacle that could undermine the growth of credit to the domestic economy and adopt more stringent

penalties related to unsafe insider and related party lending as well stating limit on government

borrowings that does not improve investment in real sectors of the domestic economy. Nigeria’s target

growth rate can be realized if needs to create a stable political and economic climate conducive to

investment is monitored.

While this analysis is specific to Nigeria and the recommendation are tailored towards it, these following

recommendations proffer possible solutions: considerations should be given to stating limits on

government borrowings from the Central Bank of Nigeria (or other banks) in absolute cash amounts or as

percentages of past revenues that is not directed to investment in real sectors of the economy and the

Central Bank of Nigeria should firmly exercise its newly gained autonomy and enforce the agreed limits

on government borrowing in order to restore the pivotal roles of the banking sector; strengthening of

institutional regulatory framework and capacity as well as maintaining effective coordination of various

regulatory efforts; and making efforts in evaluation of banking sector development is directed to the real

sector of the economy which serves as a source drivers of growth as well adopting more stringent

limitations and penalties related to unsafe insider and related party lending.

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Appendix 1: The Full Estimated VAR results

Vector Autoregression EstimatesSample (adjusted): 1983-2012Included Observations: 30 after adjustmentsStandard errors in ( ) & t-statistics in [ ]

GDPGR CCM2 CPY DCY DDM1 M2Y

GDPGR(-1)  0.161867  0.037516  0.091652 -0.021476 -0.035100  0.049925 (0.29832)  (0.03299)  (0.05127)  (0.10608)  (0.05420)  (0.04760)[ 0.54259] [ 1.13726] [ 1.78771] [-0.20244] [-0.64755] [ 1.04895]

GDPGR(-2) -0.097490  0.016619  0.014011  0.020659  0.003810 -0.012664 (0.20719)  (0.02291)  (0.03561)  (0.07368)  (0.03765)  (0.03306)[-0.47052] [ 0.72538] [ 0.39349] [ 0.28040] [ 0.10121] [-0.38310]

CCM2(-1)  4.644952  1.054016 -0.239388 -0.131495 -0.989044 -0.315527 (3.95976)  (0.43787)  (0.68050)  (1.40810)  (0.71947)  (0.63176)[ 1.17304] [ 2.40715] [-0.35178] [-0.09338] [-1.37468] [-0.49944]

CCM2(-2) -3.688096 -0.442138  0.333745 -0.181008  0.808132  0.297835 (3.10811)  (0.34369)  (0.53414)  (1.10525)  (0.56473)  (0.49588)[-1.18660] [-1.28643] [ 0.62482] [-0.16377] [ 1.43100] [ 0.60062]

CPY(-1) -2.093609  0.203794  0.478603 -0.124440 -0.809945 -0.105134 (3.28874)  (0.36367)  (0.56519)  (1.16948)  (0.59755)  (0.52470)[-0.63660] [ 0.56038] [ 0.84681] [-0.10641] [-1.35544] [-0.20037]

CPY(-2)  0.704006  0.043309  0.522030 -0.442910  0.424260  0.066081 (3.00146)  (0.33190)  (0.51582)  (1.06733)  (0.54535)  (0.47887)[ 0.23455] [ 0.13049] [ 1.01205] [-0.41497] [ 0.77795] [ 0.13799]

DCY(-1) -0.447432  0.121466  0.141460  0.764453 -0.337076 -0.001134 (1.30843)  (0.14469)  (0.22486)  (0.46528)  (0.23774)  (0.20875)[-0.34196] [ 0.83951] [ 0.62910] [ 1.64299] [-1.41785] [-0.00543]

DCY(-2)  1.279067 -0.084260 -0.198184 -0.344502 -0.017622 -0.227469 (1.43888)  (0.15911)  (0.24728)  (0.51167)  (0.26144)  (0.22957)[ 0.88893] [-0.52957] [-0.80146] [-0.67329] [-0.06740] [-0.99087]

DDM1(-1) -2.195292 -0.099621  0.376359  0.173307  0.448053  0.371871 (2.53955)  (0.28082)  (0.43643)  (0.90307)  (0.46143)  (0.40517)[-0.86444] [-0.35475] [ 0.86235] [ 0.19191] [ 0.97102] [ 0.91781]

DDM1(-2)  1.727907 -0.074404  0.061929 -0.233525 -0.025355 -0.039518

 (2.19101)  (0.24228)  (0.37653)  (0.77913)  (0.39810)  (0.34956)[ 0.78864] [-0.30710] [ 0.16447] [-0.29973] [-0.06369] [-0.11305]

M2Y(-1)  5.110224 -0.131320  0.401639  0.014942  0.802208  0.814722 (3.21068)  (0.35504)  (0.55177)  (1.14173)  (0.58337)  (0.51225)[ 1.59163] [-0.36988] [ 0.72791] [ 0.01309] [ 1.37513] [ 1.59049]

M2Y(-2) -2.556951 -0.236219 -0.760058  0.637209  0.312922 -0.207034 (4.04392)  (0.44718)  (0.69497)  (1.43803)  (0.73477)  (0.64519)[-0.63229] [-0.52825] [-1.09366] [ 0.44311] [ 0.42588] [-0.32089]

C -0.186052  0.220234 -0.230454  0.167530  0.308698 -0.081067 (1.95104)  (0.21575)  (0.33530)  (0.69379)  (0.35450)  (0.31128)[-0.09536] [ 1.02080] [-0.68732] [ 0.24147] [ 0.87081] [-0.26043]

 R-squared  0.583115  0.957475  0.920528  0.584205  0.945283  0.925248 Adj. R-squared  0.288843  0.927458  0.864431  0.290703  0.906659  0.872482 Sum sq. resids  0.603069  0.007374  0.017811  0.076260  0.019909  0.015351 S.E. equation  0.188347  0.020827  0.032368  0.066977  0.034222  0.030050 F-statistic  1.981554  31.89736  16.40942  1.990464  24.47411  17.53492 Log likelihood  16.03567  82.09619  68.86883  47.05389  67.19819  71.09861 Akaike AIC -0.202378 -4.606413 -3.724588 -2.270259 -3.613212 -3.873241 Schwarz SC  0.404808 -3.999227 -3.117403 -1.663074 -3.006027 -3.266055 Mean dependent  0.237901  0.242332  0.136456  0.189430  0.623336  0.204715 S.D. dependent  0.223345  0.077329  0.087910  0.079526  0.112013  0.084150

 Determinant resid covariance (dof adj.)  5.94E-19 Determinant resid covariance  1.97E-20 Log likelihood  425.2185 Akaike information criterion -23.14790 Schwarz criterion -19.50479

Variance Decomposition of GDPGR

 Period S.E. GDPGR CCM2 CPY DCY DDM1 M2Y

 1  0.188347  100.0000  0.000000  0.000000  0.000000  0.000000  0.000000 2  0.252639  57.14606  31.21037  2.119638  0.171026  0.680457  8.672452 3  0.279747  49.85856  27.74157  10.83243  3.522737  0.804671  7.240037 4  0.281758  49.18114  27.86454  10.79305  4.210702  0.795775  7.154795 5  0.286126  48.40229  27.09807  11.90091  4.331491  1.125801  7.141436 6  0.287267  48.31693  26.93761  12.01964  4.298142  1.320715  7.106964 7  0.289597  48.59470  26.51206  11.85120  4.420434  1.338498  7.283100 8  0.292093  48.97179  26.06221  11.83375  4.510695  1.347321  7.274238 9  0.293967  49.28665  25.73745  11.89008  4.515949  1.363612  7.206264

 10  0.295545  49.57247  25.46585  11.94658  4.507225  1.368079  7.139798

Variance Decomposition of CCM2

 Period S.E. GDPGR CCM2 CPY DCY DDM1 M2Y

 1  0.020827  1.261915  98.73808  0.000000  0.000000  0.000000  0.000000 2  0.033128  1.237423  92.26112  4.879937  0.863287  0.425169  0.333069 3  0.044020  0.717597  85.87322  9.139821  1.436553  1.748456  1.084356 4  0.054917  0.462137  79.62366  12.75543  2.108347  2.890709  2.159716

 5  0.065377  0.629666  72.81547  15.86143  3.463489  3.437912  3.792034 6  0.074845  1.400818  66.47658  17.70445  5.101843  3.780455  5.535849 7  0.083083  2.823677  60.83649  18.75461  6.690908  4.056251  6.838067 8  0.089989  4.875056  55.83740  19.34639  8.197211  4.220347  7.523593 9  0.095290  7.255034  51.75757  19.43345  9.526969  4.280850  7.746130

 10  0.098845  9.608402  48.80810  19.04347  10.54264  4.290684  7.706713

Variance Decomposition of CPY

 Period S.E. GDPGR CCM2 CPY DCY DDM1 M2Y

 1  0.032368  15.44605  17.63458  66.91937  0.000000  0.000000  0.000000 2  0.047617  7.328337  32.20427  54.77255  1.472659  2.714169  1.508015 3  0.057132  13.95673  38.04226  38.99557  1.575810  4.322017  3.107606 4  0.067343  18.46452  39.80375  28.15423  3.191095  5.265833  5.120568 5  0.078243  21.00125  41.56243  21.30520  4.578497  5.838978  5.713638 6  0.088727  22.60863  42.10925  17.45072  5.897104  6.054954  5.879339 7  0.098098  23.79501  42.05700  15.06572  7.062005  6.160935  5.859334 8  0.105949  24.86151  41.67557  13.37872  7.936614  6.281409  5.866177 9  0.112455  26.08160  40.99826  12.06114  8.587967  6.408788  5.862240

 10  0.117860  27.49580  40.05738  11.01339  9.062112  6.520299  5.851023

Variance Decomposition of DCY

 Period S.E. GDPGR CCM2 CPY DCY DDM1 M2Y

 1  0.066977  53.67551  2.820120  23.85331  19.65106  0.000000  0.000000 2  0.083258  55.87105  1.927113  21.93476  20.13273  0.133673  0.000683 3  0.085224  55.04142  2.537837  20.96301  20.00637  0.189544  1.261811 4  0.088560  51.09422  4.009927  22.26334  18.85828  0.196781  3.577456 5  0.093443  46.12937  5.773271  24.84894  17.82055  0.292120  5.135752 6  0.099102  41.32408  7.476814  28.52709  16.95053  0.437589  5.283901 7  0.104426  37.49212  8.284256  32.17000  16.53016  0.481999  5.041464 8  0.107743  35.31843  8.432786  34.38162  16.53035  0.478525  4.858290 9  0.109101  34.45083  8.308210  35.33942  16.63653  0.472163  4.792847

 10  0.109529  34.18249  8.291987  35.58761  16.70119  0.468488  4.768236

Variance Decomposition of DDM1

 Period S.E. GDPGR CCM2 CPY DCY DDM1 M2Y

 1  0.034222  7.473154  65.32738  1.691723  0.023301  25.48445  0.000000 2  0.055185  9.580510  58.30232  12.78914  1.481441  13.36746  4.479134 3  0.071513  8.834158  56.50303  13.66751  5.373309  9.244726  6.377270 4  0.085004  7.526274  56.57674  12.73082  6.121503  8.414962  8.629701 5  0.098774  8.463395  54.37374  13.38071  7.107736  7.974125  8.700295 6  0.111675  10.37038  50.07345  14.74002  8.659058  7.391925  8.765162 7  0.121861  12.47274  46.67853  15.04896  10.09546  7.020175  8.684137

 8  0.128939  14.50443  44.16161  14.71859  11.12830  6.868134  8.618932 9  0.133716  16.62365  42.17702  14.09530  11.85082  6.804077  8.449137

 10  0.136916  18.68494  40.56930  13.46912  12.27566  6.751402  8.249576

Variance Decomposition of M2Y

 Period S.E. GDPGR CCM2 CPY DCY DDM1 M2Y

 1  0.030050  26.66115  6.495433  39.96852  2.163229  1.237950  23.47373 2  0.041637  14.21113  30.39267  27.79916  1.782922  5.472298  20.34183 3  0.054535  17.40880  34.27749  19.96647  3.453329  5.774523  19.11939 4  0.069626  19.77037  33.97249  19.02976  6.952994  5.125889  15.14849 5  0.081825  19.98399  34.10096  19.32591  8.843838  4.878275  12.86703 6  0.090837  20.13008  34.22165  19.40211  10.12777  4.817342  11.30105 7  0.096806  20.49505  34.02446  19.04803  11.05938  4.862159  10.51092 8  0.100548  21.26976  33.62485  18.30219  11.72108  4.964588  10.11753 9  0.102936  22.35731  32.97883  17.53386  12.12964  5.072214  9.928142

 10  0.104716  23.66059  32.15913  16.97376  12.31111  5.139659  9.755759

Cholesky Ordering: GDPGR, CCM2, CPY, DCY, DDM1, M2Y

Appendix 2: The Impulse Response Function

-.2

-.1

.0

.1

.2

.3

1 2 3 4 5

Response of GDPGR to GDPGR

-.2

-.1

.0

.1

.2

.3

1 2 3 4 5

Response of GDPGR to CCM2

-.2

-.1

.0

.1

.2

.3

1 2 3 4 5

Response of GDPGR to CPY

-.2

-.1

.0

.1

.2

.3

1 2 3 4 5

Response of GDPGR to DCY

-.2

-.1

.0

.1

.2

.3

1 2 3 4 5

Response of GDPGR to DDM1

-.2

-.1

.0

.1

.2

.3

1 2 3 4 5

Response of GDPGR to M2Y

-.04

.00

.04

.08

1 2 3 4 5

Response of CCM2 to GDPGR

-.04

.00

.04

.08

1 2 3 4 5

Response of CCM2 to CCM2

-.04

.00

.04

.08

1 2 3 4 5

Response of CCM2 to CPY

-.04

.00

.04

.08

1 2 3 4 5

Response of CCM2 to DCY

-.04

.00

.04

.08

1 2 3 4 5

Response of CCM2 to DDM1

-.04

.00

.04

.08

1 2 3 4 5

Response of CCM2 to M2Y

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of CPY to GDPGR

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of CPY to CCM2

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of CPY to CPY

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of CPY to DCY

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of CPY to DDM1

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of CPY to M2Y

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of DCY to GDPGR

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of DCY to CCM2

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of DCY to CPY

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of DCY to DCY

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of DCY to DDM1

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of DCY to M2Y

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of DDM1 to GDPGR

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of DDM1 to CCM2

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of DDM1 to CPY

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of DDM1 to DCY

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of DDM1 to DDM1

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of DDM1 to M2Y

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of M2Y to GDPGR

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of M2Y to CCM2

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of M2Y to CPY

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of M2Y to DCY

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of M2Y to DDM1

-.08

-.04

.00

.04

.08

1 2 3 4 5

Response of M2Y to M2Y

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

Cholesky Ordering: GDPGR, CCM2, CPY, DCY, DDM1, M2Y