Finance Growth Jde04

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Financial development and economic growth:

evidence from panel unit root and cointegration tests

Dimitris K. Christopoulosa,*, Efthymios G. Tsionas b

a

Department of Economic and Regional Development, Panteion University, Leof. Syngrou 136, 17671, Athens, Greece

b Department of Economics, Athens University of Economics and Business, Athens, Greece

Received 1 November 2001; accepted 1 March 2003

Abstract

In this paper we investigate the long run relationship between financial depth and economic

growth, trying to utilize the data in the most efficient manner via panel unit root tests and panel

cointegration analysis. In addition, we use threshold cointegration tests, and dynamic panel dataestimation for a panel-based vector error correction model. The long run relationship is estimated

using fully modified OLS. For 10 developing countries, the empirical results provide clear support

for the hypothesis that there is a single equilibrium relation between financial depth, growth and

ancillary variables, and that the only cointegrating relation implies unidirectional causality from

financial depth to growth.

D 2003 Elsevier B.V. All rights reserved.

JEL classification: C23; O16; O40; G28

Keywords: Financial development; Growth; Panel unit roots; Panel cointegration; Threshold cointegration

1. Introduction

A large and expanding literature tries to shed some light on the roles of policy or

‘‘ancillary’’ variables in the determination of economic growth. Most of this literature has

mainly focused on the role of macroeconomic stability, inequality, income and wealth,

institutional development, ethnic and religious diversity and financial market imperfec-

tions. For an extensive survey of this literature, see Levine (1997). Among these factors

0304-3878/$ - see front matter D 2003 Elsevier B.V. All rights reserved.

doi:10.1016/j.jdeveco.2003.03.002

* Corresponding author. Tel.: +30-210-9224948; fax: +30-210-9229312.

E-mail address: [email protected] (D.K. Christopoulos).

www.elsevier.com/locate/econbase

Journal of Development Economics 73 (2004) 55–74



the role of financial markets in the growth process has received recently considerable

attention. In this framework, financial development is considered by many economists to

be of paramount importance for output growth. In particular, government restrictions on

the banking system (such as interest rate ceiling, high reserve requirements and directedcredit progr ams) hinder f inancial development and reduce output growth, see McKinnon

(1973) and Shaw (1973).

Likewise, the endogenous growth literature stresses the influence of financial markets

on economic growth, see among others Bencivenga et al. (1995), Greenwood and Smith

(1997), and Obstfeld (1994). These authors include financial intermediaries, information

collection and analysis, risk sharing etc in the proposed models. In this line of research,

Benhabib and Spiegel (2000) argue that a positive relationship is expected also to exist

between financial development and total factor productivity growth and invest ment.

However, their empirical results are very sensitive to model specification. Further, Beck

et al. (2000) find that financial development has a large positive impact on total factor

productivity (TFP), which feeds through to overall GDP growth. See also Neusser and

Kugler (1998).

A problem with the previous studies is that a positive relationship between financial

development and output growth can exist for different reasons. As output increases the

demand for financial service increases too, which in turn has a positive effect on financial

development. Other things being equal, it is financial development that follows output

growth and not the opposite. This issue was considered in Robinson (1952, p. 86). Others

were keener to totally dismiss the impact of financial development on economic growth.

Lucas (1988, p. 6) states, for example, that ‘‘the importance of financial matters is verybadly overstressed ’’ while Chandavarkar (1992, p. 134) notes ‘‘none of the pioneers of

development economics. . . even list finance as a factor of development ’’. See also Luintel

and Khan (1999).

Although many empirical studies have investigated the relationship between financial

depth, defined as the level of development of financial markets, and economic growth, the

results are ambiguous. On the one hand, cross country and panel data studies find positive

effects of financial development on output growth even after accounting for other

determinants of growth as well as for potential biases induced by simultaneity, omitted

variables and unobserved country-specific effect on the finance-growth nexus, see for

example King and Levine (1993a,b), Khan and Senhadji (2000) and Levine et al. (2000).On the other hand, time series studies give contradictory results. Demetriades and Hussein

(1996) find little systematic evidence in favor of the view that finance is a leading factor in

the process of economic growth. In addition they found that for the majority of the

countries they examine, causality is bi-directional, while in some cases financial devel-

opment follows economic growth. Luintel and Khan (1999) used a sample of ten less

developed countries to conclude that the causality between financial development and

output growth is bi-directional for all countries. All these results show that a consensus on

the role of financial development in the process of economic growth does not so far exist.

There are a number of concerns with previous empirical work. Although the nature of

I (1) variables has been recognized as critical, and proper estimation techniques (organizedaround unit roots and cointegration) have been used, the small samples typically used may

significantly distort the power of standard tests, and lead to misguided conclusions. Thus,

D.K. Christopoulos, E.G. Tsionas / Journal of Development Economics 73 (2004) 55–7456



all efforts must be made to utilize the data in the most efficient manner in order to draw

sharp inferences. This is true not only for unit root but also for cointegration inferences.

Even with time series tests, however, the possibility exists that there are threshold effects

in a possible cointegrating relationship between output and financial development. Indeed,it could be that below a level of financial development there is no effect on growth or a

small effect, and a larger effect as financial development crosses the threshold. Important

studies in the field include Levine et al. (2000) and Beck et al. (2000) who take into

account the problem of simultaneity of regressors. They used a GMM dynamic panel

estimator but they did not consider the integration and cointegration properties of the data.

Thus, it is not clear what the estimated panel models represent: Do they represent a

structural long run equilibrium relationship or a spurious one? We consider this issue of

the utmost importance, and we believe the present paper throws some light on this

question.

More specifically, this paper contributes the following:

(1) We use time series unit root tests along with panel unit root tests to examine the

stationarity properties of the data. The use of panel-based tests is necessary because

the power of standard time-series unit root tests may be quite low given the sample

sizes and time spans typically available in economics.

(2) The cointegration framework of Johansen (1988) is applied to test for multivariate

cointegrating relationships. Additionally, panel cointegration tests are conducted to

make sure that Johansen tests do not suffer power loss due to finite samples.

(3) We take into account the possibility that the relationship between economic activityand financial development may involve a ‘‘threshold effect’’, see Berthelemy and

Varoudakis (1996) and Deidda and Fatouh (2002). It could be that below a level of

financial development there is no effect on growth, and a sizeable effect above the

threshold. Further, the presence of a threshold affects seriously the stationarity

properties of the data. According to Enders and Granger (1998) standard tests of

integration and cointegration have lower power in the presence of misspecified

dynamics. This calls for application of cointegration tests that account for possible

threshold effects in the long run relationship if we are to obtain sensible results.

(4) Cointegrating vectors are estimated using the fully modified (FM) OLS estimation

technique for heterogeneous cointegrated panels (Pedroni, 2000). This methodologyallows consistent and efficient estimation of cointegrating vectors. We argue below

why this methodology retains the flexibility of the Levine et al. (2000) approach while

at the same time: (a) allows consistency of the long-run relation with the short-run

adjustment, (b) deals with the endogeneity of regressors problem, and (c) respects the

time-series properties of the data in that integration and cointegration properties are

explicitly taken into account.

(5) We distinguish between long run and short run causality. This distinction is very

important since as Darrat (1999) states, most of the benefits of higher levels of

financial development could be realized in the short-run while in the long run as the

economy grows and becomes mature these effects slowly disappear. Thus, testing onlyfor long run causality would lead to the wrong conclusion, namely absence of any

casual relationship between financial development and output growth. To this end, we

D.K. Christopoulos, E.G. Tsionas / Journal of Development Economics 73 (2004) 55–74 57



specify and estimate an error correction model (ECM) appropriate for heterogeneous

panels.

The present paper addresses the empirical relationship between financial development and economic growth for ten less developed countries over the period 1970–2000. In

Section 2, we present a brief literature review. The econometric techniques are presented in

Section 3. The results are discussed in Section 4. The paper concludes with a summary of

methodology and results.

2. A review on the relationship between financial development and growth

There is no general agreement among economists that financial develo pment is

beneficial for growth. In a simple endogenous growth model, Pagano (1993) uses the

AK model to conclude that the steady state growth rate depends positively on the

percentage of savings diverted to investment, so one channel through which financial

deepening affects growth is converting savings to investment. Berthelemy and Varoudakis

(1996) use a theoretical model with banks acting as Cournot oligopolists to find that, in the

stable equilibrium, the growth rate depends positively on the number of banks, or the

degree of competitiveness of the financial system. Their results show that educational

development is a pre-condition of growth, and financial underdevelopment is an obstacle

when the educational system is not successful. Greenwood and Jovanovic (1990) consider

a model that allows examining the relation between growth and income distribution, aswell as between financial structure and economic development. The fundamental reason

for a positive effect of financial structure on growth is the more efficient undertaking of

investment, and more efficient capital allocation because agents can have better informa-

tion about the nature of shocks (aggregate versus idiosyncratic) that hit particular projects.

This is more or less consistent with the classical view on the relation between growth and

financial development. Levine (1991) considers an endogenous growth model with stock

markets, and shows that they accelerate growth for two reasons: First, because ownership

of firms can be traded without disrupting the production process. Second, because agents

are allowed to diversify portfolios. The model has the reasonable implication that in the

absence of stock markets, agents would be discouraged to invest because of risk aversion.Also ‘‘they accelerate growth directly by eliminating premature capital liquidation which

increases firm productivity and indirectly by reducing liquidity risk which encourages firm

investment ’’ (p. 1459).

Singh (1997) claims that financial development may be not be beneficial for growth for

several reasons. He states that ‘‘ first, the inherent volatility and arbitrariness of the stock

market pricing process under DC [developing countries] conditions make it a poor guide

to efficient investment allocation. Secondly, the interactions between the stock and

currency markets in the wake of unfavorable economic shocks may exacerbate macro-

economic instability and reduce long-term growth. Thirdly, stock market development is

likely to undermine the existing group-banking systems in DC’s, which, despite their manydifficulties, have not been without merit in several countries, not least in the highly

successful East Asian economies’’ (pp. 779–780).




On the empirical side, King and Levine (1993a) use IMF data and various financial

indicators to conclude that there is a positive relationship between financial indicators and

growth, and that financial development is robustly correlated with subsequent rates of

growth, capital accumulation, and economic efficiency. They correctly emphasize that policies that alter the efficiency of financial intermediation exert a first-order influence on

growth. This is a standard implication of models of endogenous growth with financial

intermediation. Atje and Jovanovic (1993) examine the role of stock markets on

development, and conclude that there is positive effect on the level as well as on the

growth. They could not, however, establish a significant relationship between bank

liabilities and growth. Levine and Zervos (1996) use various measures of stock market

development, and conclude that there is a significant relationship. When they include

banking depth variables in their regressions, they turn out to be non-significant. They

emphasize their results are indicative of partial correlation only, and more research would

be needed in the area. Arestis and Demetriades (1997) use time series analysis (whereas

previous studies use cross-section data) and Johansen cointegration analysis for the US

and Germany. For Germany, they find an effect of banking development growth. In the

US, there is insufficient evidence to claim a growth effect of financial development, and

the data point to the direction that real GDP contributes to both banking system and stock

market development. In this line of research Neusser and Kugler (1998) used manufac-

turing data from thirteen OECD countries over the period 1970–1991 to analyze the

existence of a long-run relationship between manufacturing sector GDP and financial

sector GDP as well as between manufacturing TFP and financial sector GDP. To this end,

different tests were performed including Johansen maximum likelihood and residual-based panel cointegration tests. They found that for the majority of countries they examine a

cointegration relationship could be established not so much between financial sectors,

GDP and manufacturing GDP but mostly between financial sector GDP and manufactur-

ing TFP. Subsequent causality tests gave mixed results. For some countries financial

activity causes manufacturing GDP, for others financial sector causes both manufacturing

GDP and TFP while for others a feedback exists from manufacturing to financial sector.

Levine et al. (2000), using a sample of 74 developed and less developed countries over

the period 1960– 1995, go beyond previous studies recognizing the potential biases

induced by simultaneity, omitted variables and unobserved country-specific effect on

the finance growth nexus. According to these authors, this issue is of paramount importance for settling the question of causality. To deal effectively with theses problems,

they suggest the use of estimators appropriate for dynamic panels like GMM as well as

cross-sectional instrumental variable estimators where legal rights of creditors, the

soundness of contract enforcement and the level of corporate accounting standards are

used as instruments to extract the exogenous component of financial development. Both

estimation techniques correct for biases associated with previous studies of the financial

development-growth relation. At the same time, they offer more precise estimates. They

found that the strong positive relationship between financial development and output

growth can be partly explained by the impact of the exogenous components like finance

development on economic growth. Levine et al. (2000) interpreted these results assupportive of the growth-enhancing hypothesis of financial development. These results

are in line with those reported by Levine (1999) in a sample of 49 countries over the period




1960–1989 where GMM procedures are used to find that the exogenous components of

financial development (national, legal, and regulatory characteristics) have a positive

influence on economic growth. The results could imply that the direction of causality

between financial development and economic growth runs in both directions. Beck et al.(2000) investigate not only the relationship between financial development and economic

growth but also the relationship between financial development and the sources of growth

in terms of private saving rates, physical capital accumulation, and total factor produc-

tivity. Once again, GMM and IV estimators were used to correct for possible simultaneity

biases. They conclude that higher levels of financial development lead to higher rates of

economic growth, and total factor productivity. For the remaining variables, they could not

document any relationship with financial development. Further, Levine (1998) using a

sample of 44 developed and less developed countries during the period 1975– 1993,

examines the links between banking development and long-run economic growth. The

usual GMM estimation procedure is used to account for simultaneity bias. The degree to

which legal codes emphasize the rights of creditor and the efficiency of the legal system in

enforcing laws and contracts are considered as instruments. The empirical evidence is

supportive for a strong positive relation between the exogenous component of banking

development with output growth, physical accumulation and productivity growth.

Finally, Demirgucß-Kunt and Maksimovic (1998) estimate a financial planning model to

find that financial development facilitates the firm’s growth. In this context an active stock,

market and a well-developed legal system are crucial for the further development of the

firms.

The empirical studies reviewed in this section are subject to a number of limitations: (a)With time series data, although the nature of I (1) variables has been recognized as critical,

and proper estimation techniques (organized around unit root and cointegration) have been

used, the small samples used may significantly distort the power of standard tests, and lead

to misguided conclusions. (b) The cointegration tests including panel cointegration tests

have lower power in the presence of misspecified dynamics, see Enders and Granger

(1998). (c) An issue of simultaneity arises: To this end, Levine et al. (2000) and Beck et al.

(2000) proposed the use of GMM dynamic panel estimators. However, in this approach,

the integration and cointegration properties of the data are ignored. Thus, it is not clear that

the estimated panel models represent a structural long run equilibrium relationship instead

of a spurious one. Within this context, the imposition of homogeneity assumptions on thecoefficients of lagged dependent variable in panel data estimators could lead to serious

biases (Kiviet, 1995).

Neusser and Kugler (1998) and Levine et al. (2000) represent two different poles in the

literature. Neusser and Krugler focuses on time series properties of the data ignoring the

simultaneity issue, while Levine et al. (2000) deal with simultaneity without accounting

for the time series properties of the data. An alternative is explored in this paper. This

alternative consists briefly in the following: In Levine et al. (2000) estimation is conducted

in two steps, first a cross-sectional regression of growth on finance and ancillary

regressors, and GMM in the second stage to address simultaneity. In our estimation

approach, we exploit both the cross-sectional and time-series dimension of the data byusing panel cointegration techniques. In that way we can address the simultaneity issues of

the regressors but we also have another important advantage relative to previous research.




In Levine et al. (2000), the first-pass cross-sectional regression represents the long-run

regression while the second-pass regression (estimated by GMM) captures the short-run

dynamics. The two regressions, however, are not connected as they should: One would

expect that the second-pass regression can be derived from the long-run model byappropriate restrictions but this does not seem possible within the Levine et al. (2000)

framework. More importantly, Levine et al. (2000) do not formally test that the first-pass

regression is valid so it is not certain that it represents something structural. It is, therefore,

not certain whether the second-stage regression represents an adjustment to the long-run

equilibrium implied by the first stage. Within the panel cointegration framework used in

this paper, we are able to address these important issues, and at the same time we retain the

flexibility of the Levine et al. (2000) approach in that we are able to provide long-run

estimates, short-run adjustments, and address the endogeneity issues by formally treating

all variables as part of a vector autoregression in the context of testing for cointegration,

and estimating panel cointegrating regressions. More importantly, we can formally test

whether there is indeed a structural, long run relationship between financial development

and growth. We consider this of the utmost importance: Without such a structural

relationship, short run dynamics will be misleading.

3. The model and econometric techniques

To investigate the relationship between growth and financial depth, we use the

following model yit ¼ b0i þ b1i F it þ b2iS it þ b3i ˙ pit þ uit ð1Þ

where yit is real output in country i and year t , F it is a measure of financial depth, S it is the

output share of investment, pit is inflation, and uit is an error term. Since the direction of

causality is not clear we also specify the model

F it ¼ b0i þ b1i yit þ b2iS it þ b3i ˙ pit þ v it ð2Þ

Both equations are to be considered as long run, or equilibrium relations. We may, of

course, have more cointegrating relations involving inflation or investment share as thedependent variable. Provided all variables involved are integrated of order one, or I (1),

valid economic inferences can be drawn only if these relations (or perhaps more, having

investment share or inflation as dependent variable) are cointegrating relations,

otherwise spurious inferences would result. Previous studies have examined cointegra-

tion on country by country basis by using time-series techniques, like Dickey-Fuller

tests, and Johansen’s maximum likelihood cointegration methodology. However, given

the short span of the data, we need to utilize information in the most efficient way, and

make use of panel-based unit root and cointegration tests as well. In our empirical

analysis, we will use pure time series tests and procedures as well, for comparison

purposes.Regarding the data, y is the quantity of output expressed as an index number

(1995 = 100), finance depth ( F ) is the ratio of total bank deposits liabilities to nominal




GDP and the share of investment, S , is the share of gross fixed capital formation to

nominal GDP. Inflation rate ( p ) is measured using the consumer price index. All data is

drawn from issues of the International Financial Statistics published by the International

Monetary Fund over the period 1970–2000. We use data for ten developing countries,namely Colombia, Paraguay, Peru, Mexico, Ecuador, Honduras, Kenya, Thailand,

Dominican Republic, and Jamaica. Regar ding the measurement of financial deepening

we have followed Luintel and Khan (1999). The selection of countries was dictated by the

requirement of having continuous data records over the period 1970–2000, and it is the

same set of countries used by De Mello (1999) in a different context. In the following, we

describe the panel-based econometric procedures.

3.1. Testing for integration

Before proceeding to the identification of a possible long run relationship we need to

verify that all variables are integrated of order one in levels. However, since the power of

individual unit root tests can be distorted when the span of the data is short (Pierse and

Shell, 1995), we have used panel unit root tests due to Im et al. (1997) and Maddala and

Wu (1999). These are denoted by IPS and MW, respectively. In both tests, the null

hypothesis is that of a unit root.

The IPS statistic is based on averaging individual Dickey-Fuller unit root tests (t i)

according to

t IPS ¼ ffiffiffiffi N p

ðt

ÀE

½t i

jqi

¼0�Þ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

var ½t i j qi ¼ 0�p ! N ð0; 1Þ ð3Þ

where t ¼ N À1P N

i¼1 t i . The moments of E [t ijqi =0] and var[t ijqi = 0] are obtained by

Monte Carlo simulation and are tabulated in IPS.

The MW statistic is given by P ¼ À2P N

i¼1 ln pi , and combines the p-values from

individual ADF tests. The P test is distributed as v2 with degrees of freedom twice the

number of cross section units, i.e. 2 N , under the null hypothesis.

Breitung (1999) finds that IPS suffers a dramatic loss of power when individual

trends are included, and the test is sensitive to the specification of deterministic trends.

The MW test has the advantage over the IPS that its value does not depend ondifferent lag lengths in the individual ADF regressions. In addition Maddala and Wu

(1999) and Maddala et al. (1999) found that the MW test is superior compared to the

IPS test.

3.2. Testing for cointegration

The next step is to test for the existence of a long run relationship among y, F and the

control variables S and p. A common practice to test for cointegration is Johansen’s

procedure. However, the power of the Johansen test in multivariate systems with small

sample sizes can be severely distorted. To this end, we need to combine information fromtime series as well as cross-section data once again. In this context three panel

cointegration tests are conducted.




First , we use a test due to Levin and Lin (1993) in the context of panel unit roots, to

estimated residuals from (supposedly) long run relations. Levin and Lin (1993) consider

the model

yit ¼ qi yi;t À1 þ z it Vc þ uit ð4Þwhere z it are deterministic variables, uit is iid (0,r2) and qi = q. The test statistic is a t -

statistic on q given by

t q ¼ðqÀ 1Þ

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiX N

i¼1

XT

t ¼1

y2i;t À1

s

se

ð5Þ

where

˜ yit ¼ yit ÀXT

s¼1

hðt ; sÞ yis; uit ¼ uit ÀXT

s¼1

hðt ; sÞuis hðt ; sÞ ¼ z t VXT

t ¼1

z t z t V

! z s;

s2e ¼ ð NT ÞÀ1

X N

i¼1

XT

t ¼1

u2it ;

and q is the OLS estimate of q. It can be shown that if there are only fixed effects in the

model, then

ffiffiffiffi N

p T ðq À 1Þ þ 3

ffiffiffiffi N

p ! N 0;

51

5

ð6Þ

and if there are fixed effects and a time trend,

ffiffiffiffi N

p ðT ðqÀ 1Þ þ 7:5Þ ! N 0;

2895

112

ð7Þ

Second , we use the unit root tests developed for Eq. (4) by Harris and Tzavalis (1999).

If there are only fixed effects in the model, then

ffiffiffiffi N

p q À 1 þ 3

T þ 1

! N 0;

3ð17T 2 À 20T þ 17Þ5ðT À 1ÞðT þ 1Þ3

!ð8Þ

If there are fixed effects and a time trend, then

ffiffiffiffi N

p q À 1 þ 15

2ðT þ 2Þ

! N 0;

15ð193T 2 À 728T þ 1147Þ112ðT þ 2Þ3ðT À 2Þ

!ð9Þ

It must be noted that Levin and Lin (1993) tests may have substantially size distortion if there is cross-sectional dependence (O’Connell, 1998). Also, Harris and Tzavalis (1999)

find that small T yields Levin and Lin tests which are substantially undersized and have




low power. A drawback of the Levin and Lin or Harris and Tzavalis tests is that they do

not allow for heterogeneity in the autoregressive coefficient, q.

Finally, to overcome the problem of heterogeneity that arises in both tests we use Fisher’s

test to aggregate the p-values of individual Johansen maximum likelihood cointegration test statistics, see Maddala and Kim (1998, p. 137). If pi denotes the p-value of the Johansen

statistic for the ith unit, then we have the result À2P N

i¼1 log pifv22 N . The test is easy to

compute and, more importantly, it does not assume homogeneity of coefficients in different

countries.

3.3. Testing for unit roots in threshold autoregressive models

We use tests for unit roots from threshold autoregressive (TAR) models, following

Caner and Hansen (2001) on which this section largely draws. The model is given by a

TAR(k ) of the form

D yt ¼ h1 V xt À11ð z t À1VkÞ þ h2 V xt À11ð z t À1zkÞ þ ut ð10Þ

where yt is the series we consider, k is the threshold parameter, xt À 1=( yt À 1,r t À 1 V ,

D yt À 1,. . .,D yt À k ) V, z t u yt À yt À m for some mz 1 (m = 1 in this application), and

r t À 1 is a vector of exogenous variables, a constant in our case. The procedure can

be used to test simultaneously for stationarity as well as threshold effects. First,

model (10) is estimated by OLS for a fixed ka[k,k], and the residual variance

s

2

ðkÞ ¼ T À1 PT

t ¼1 uðkÞ2

is computed.The threshold parameter is estimated by k ¼ agrmin: s2ðkÞk. For the estimate k, the

residuals u t and the residual variance s2 are computed. To test for a threshold effect, we

need to test the hypothesis H 0: h1 = h2 which can be tested using a Wald test W = T ( s02/

s2À 1) where s02 denotes the residual variance under the null (i.e. the residual variance

computed using OLS in the linear model under the null hypothesis). However, the

distribution of the Wald test is non-standard. Caner and Hansen (2001) provide the correct

asymptotic distribution but point out that for small sample inference, model-based

bootstrap approach should be used. We have used bootstrapping to compute exact p-

values of the test, using 10,000 replications. It is also possible to compute Wald tests for

threshold effects in specific coefficients. To test for unit roots, we use the one-sidedformulation of Caner and Hansen (2001), namely H 0: q1 = q2 = 0 versus the alternative H 1:

q1 < 0 or q2 < 0 where qi denotes the first element of hi. The test statistic is a two sided

Wald test of the form R1T = t 12 + t 2

2 where t i signifies the t -ratios for qi from OLS regression

in the TAR model. Exact p-values for this test can be computed using the bootstrap

approach. Since exact p-values are available, a panel data version of the Caner and Hansen

(2001) tests can be constructed by considering an MW formulation of the form

À2P N

i¼1 log pifv22 N , where pi denotes the p-value of a given Wald test for the ith country.

3.4. Estimating the long run relationship

Having established that the dependent variable is structurally related to the explanatory

variables, and thus a long run equilibrium relationship exists among these variables, we




proceed to estimate the Eq. (1) by the method of f ully modified OLS appropriate for

heterogeneous cointegrated panels (Pedroni, 2000). This methodology addresses the

pro blem of non-stationar y regressors, as well as the problem of simultaneity bias raised

by Levine et al. (2000): OLS estimation yields biased results because, in general, theregressors are endogenously determined in the I (1) case. We consider the following

cointegrated system for panel data

yit ¼ ai þ xit Vbþ uit ð11Þ

xit ¼ xi;t À1 þ eit ð12Þ

where nit =[uit ,eit V] is stationary with covariance matrix Xi. Following Phillips and Hansen

(1990) a semi-parametric correction can be made to the OLS estimator that eliminates the

second order bias caused by the fact that the regressors are endogenous. Pedroni (2000)follows the same principle in the panel data context, and allows for the heterogeneity in the

short run dynamics and the fixed effects. Pedroni’s estimator is

b FM À b ¼X N

i¼1

XÀ222i

XT

t ¼1

ð xit À ¯ xt Þ2

!À1

ÁX N

i¼1

XÀ111iX

À122i

XT

t ¼1

ð xit À ¯ xt Þuit * À T ci

!

ð13Þ

uit

*¼

uit À

XÀ

1

22iX

21i; c

i ¼C

21i þX0

21i ÀX

À1

22iX

21iðC

22i þX0

22iÞ ð14

Þwhere the covariance matrix can be decomposed as Xi =Xi

0 +Ci +Ci where Xi

0 is the

contemporaneous covariance matrix, and Ci is a weighted sum of autocovariances. Also,

X i

0 denotes an appropriate estimator of Xi

0.

4. Empirical results

Time series ADF tests are reported in Table 1 for all 10 countries. All time series

involved contain unit roots according to the ADF test, save for output in Ecuador andJamaica, finance depth in Thailand, the share of investment in Colombia, Paraguay, Peru,

and the Dominican Republic, as well as inflation in Thailand. ADF tests in first differences

show that for most of these series, their first differences are stationary so the conclusion of

the ADF test is not safe. Possible exceptions are output series for Columbia, Thailand and

Dominican Republic finance depth for Thailand and Honduras, and investment share for

Paraguay, Kenya and Thailand. However, panel unit roots tests (both IPS and MW),

reported in Table 2, support the hypothesis of a unit root in all variables across countries,

as well as the hypothesis of zero order integration in first differences.

Country by country Johansen maximum likelihood cointegration results are reported in

Table 3. The hypothesis of no cointegration is rejected for all countries, and the hypothesisof one cointegrating vector is accepted. Panel cointegrating tests are reported in Table 4.

The results are fairly conclusive: Fisher’s test supports the presence of one cointegrating




vector. The Harris and Tzavalis tests support the hypothesis of a cointegrating relation.

Finally, the Levin and Lin test with fixed effects supports the hypothesis of a cointegrating

relation but this is not the case when both fixed and time effects are included. When

financial depth is used as dependent variable, all tests are in agreement that cointegration

does not exist. Therefore, all in all both time series and panel-based tests agree that there

is a single cointegrating vector, and long run causality is unidirectional from financial

depth to growth.

Next, we consider Hansen’s TAR(1) model. Wald tests for the hypothesis of no

threshold effects are reported in the second column of Table 5. The hypothesis can be

rejected only for Peru and Thailand but judging from p-values (0.043 and 0.054) it cannot

be rejected at 4% or lower. So we do not have strong evidence in favor of threshold effects.

Wald tests for threshold effects in individual coefficients (namely c, b, and c) are reported

in columns 3–5. The evidence is again rather weak: At the 4% level we cannot reject that

these coefficients are the same in the two regimes defined by the threshold, as expected

from the results of the threshold effect Wald statistic in the second column. We have only

three marginal exceptions which, however, are significant at the 4% level. Finally, t -tests

Table 2

Panel unit root tests

Variables Levels First differences

IPS MW IPS MW

Output ( y) À 0.18 27.12 À 4.52*** 58.33***

Finance depth ( F ) 2.71 14.77 À 6.63*** 83.64***

Investment share (S ) À 0.04 30.37 À 5.81*** 62.98***

Inflation ( p) À 0.47 26.37 À 5.19*** 74.29***

IPS and MW are the Im, Pesaran and Shin t -test and Maddala and Wu v2 test for a unit root in the model. The

critical values for MW test are 37.57 and 31.41 at 1% and 5% statistical levels, respectively. Boldface values

denote sampling evidence in favour of unit roots. ***Signifies rejection of the unit root hypothesis at the 1%

level.

Table 1

ADF unit root tests

Country Output ( y) Finance depth ( F ) Investment share (S ) Inflation ( p)

Levels Diff Levels Diff Levels Diff Levels Diff

Colombia À 0.90 À 1.86 À 0.36 À 3.31* À 3.35* À 3.30* À 2.98 À 3.29*

Paraguay À 1.84 À 3.26* À 1.34 À 3.65** À 3.69** À 2.84 À 1.93 À 4.95***

Peru À 2.54 À 3.72** À 2.31 À 3.72** À 3.22* À 5.20*** À 2.63 À 4.07**

Mexico À 2.19 À 3.15* À 2.21 À 5.32*** À 2.47 À 3.99** À 1.49 À 3.68**

Ecuador À 4.94*** À 3.51** À 1.38 À 5.21*** À 2.08 À 3.83** À 2.28 À 3.28*

Honduras À 2.10 À 3.88** À 1.75 À 2.19 À 1.14 À 3.99* À 2.59 À 4.17**

Kenya À 0.63 À 5.85*** À 1.20 À 3.78** À 2.12 À 2.64 À 2.13 À 3.41**

Thailand À 2.57 À 2.56 À 3.99*** À 2.97 À 2.01 À 2.63 À 4.04** À 3.98**

Dominican Republic À 1.29 À 2.89 À 1.33 À 5.83*** À 3.39* À 4.71*** À 1.71 À 3.46**

Jamaica À 3.24* À 3.32* À 2.12 À 3.78** À 2.53 À 2.96 À 3.05 À 4.32***

Levels and Diff denote the augmented Dickey-Fuller t -tests for a unit root in levels and first differencesrespectively. Number of lags was selected using the AIC criterion. Boldface values denote sampling evidence in

favour of unit roots. (***), (**) and (***) signify rejection of the unit root hypothesis at the 1%, 5% and 10%

levels, respectively.




for stationarity are reported in the two last columns. Stationarity of the error correction

term (taking account of possible thresholds) is the important task of this analysis. Given

that we could not reject absence of threshold effects, it would be reasonable to expect that

our previous analysis would apply, and error correction terms would be stationary. Indeed,

only for Mexico the t 1 statistic has a p-value equal to 0.003 (the test value is 5.04) but the

t 2 statistic clearly indicates that we must accept stationarity in the other regime. Given that the two regimes do not have a large number of observations, this finding could be an

artifact. In the case of Colombia, t 1 is 4.02 with p-value equal to 0.03 so we can reject

stationarity at 5% but not at 3% so this is, again, very weak evidence. MW panel unit root

tests provide additional evidence in favor of cointegration: The mw test rejects cointegra-

tion in the case of t 1 statistic because of the small p-value for Mexico (0.003). Given that

the Wald test indicates absence of threshold effects, and the vast majority of t 1 and t 2

Table 4

Panel cointegration tests

Levin-Lin Harris-Tzavalis Fisher v2 cointegration test

F.E. F.E.T. F.E. F.E.T.

Dependent variable: output (y)

À 8.36*** 0.89 À 77.13*** À 5.57*** r = 0 r V 1 r V2 r V 3

76.09 30.73 28.91 23.26

Dependent variable: financial depth (F)

À 1.20 0.50 À 0.85 À 1.65

F.E. denotes the Levin and Lin and Harris and Tzavalis t -tests with fixed effects but no time effects in the fitted

equation while F.E.T. includes both fixed and time effects in the fitted regression. ***Signifies rejection of the

null hypothesis of no-cointegration at 2% significance level. Boldface values denote sampling evidence in favour

of unit root. The critical values for Fisher’s v2 test are 37.57 and 31.41 at 1% and 5% statistical level, respectively.

Fisher’s test is computed based on p-values from Johansen’s maximum likelihood cointegration methodology.

Therefore, it applies regardless of the dependent variable.

Table 3

Johansen Cointegration tests

Country Max eigenvalue statistic H 0: rank = r

r = 0 (62.99) r V1 (42.44) r V2 (25.32) r V3 (12.25)

Colombia 65.58a 25.83 8.02 0.25

Paraguay 115.12a 22.44 11.86 5.47

Peru 106.88a 33.83 13.74 4.83

Mexico 117.87a 26.64 13.52 5.84

Ecuador 125.14a 25.39 9.16 3.45

Honduras 141.06a 38.27 14.21 5.46

Kenya 98.83a 29.02 14.47 6.11

Thailand 66.27a 36.99 18.61 5.65

Dominican Republic 141.83a 40.86 17.32 6.50

Jamaica 97.14a 36.39 14.25 6.51

r denotes the number of cointegrating vectors. The optimal lag lengths for the VARs were selected by minimisingthe Schwarz criterion. Numbers in parentheses next to r = 0, r V 1, r V2 and r V 3 represent the 5% critical values

of the test statistic. An (a) indicates rejection of the null hypothesis of no-cointegration at 5% level of significance.




statistics favour cointegration. We conclude in favour of panel cointegration. Therefore,

we can conclude that stationarity of error correction terms seems a reasonable hypothesis,

and we can claim that our estimated relations are indeed cointegrating, long run

equilibrium relations. The next step is estimation of such relationships.Fully modified OLS estimates of the cointegrating relationship are reported in Table 6

on a per country basis as well as for the panel as a whole. For the panel, the coefficient of

financial depth is 14.13 with t -statistic of 2.57 so it is statistically significant, and the effect

is positive. The share of investment has a positive effect (0.20), and inflation has a

negative impact on growth ( À 0.04). However, inflation does not seem to be statistically

significant for growth but investment share is statistically significant at the 10% level, and

marginally so at the 5% level. On a per country basis, financial depth has a positive impact

Table 5

Testing for threshold unit roots in the error correction term (U t )

Country Wald test for Wald test for threshold effect in t -test for stationarity

threshold effect Intercept U t À 1 DU t À 1 t 1 t 2

Colombia 7.12 (0.504) 0.078 (0.950) 5.50 (0.140) 0.018 (0.940) 4.02 (0.030) 0.236 (0.755)

Paraguay 16.0 (0.074) 10.1 (0.075) 0.898 (0.598) 10.1 (0.042) 2.82 (0.131) 3.80 (0.030)

Peru 19.7 (0.043) 1.78 (0.743) 7.61 (0.084) 1.21 (0.544) À 0.52 (0.844) 2.85 (0.111)

Mexico 9.22 (0.302) 2.26 (0.655) 5.45 (0.128) 1.81 (0.429) 5.04 (0.003) 0.323 (0.758)

Ecuador 8.29 (0.408) 4.54 (0.376) 0.435 (0.723) 1.77 (0.456) 1.83 (0.305) 1.26 (0.503)

Honduras 3.97 (0.901) 1.08 (0.799) 2.22 (0.387) 0.023 (0.931) 3.30 (0.052) 0.456 (0.692)

Kenya 8.18 (0.395) 6.77 (0.158) 2.95 (0.314) 1.04 (0.558) 2.38 (0.168) 2.45 (0.152)

Thailand 16.0 (0.054) 6.17 (0.215) 0.470 (0.708) 0.009 (0.956) 3.25 (0.052) 1.90 (0.340)

Domin.

Republic

8.64 (0.343) 0.015 (0.982) 6.84 (0.069) 0.335 (0.740) 2.83 (0.093) À 1.55 (0.974)

Jamaica 7.63 (0.457) 3.13 (0.553) 0.965 (0.582) 3.00 (0.301) 1.58 (0.39) 0.997 (0.539)MW panel

test

28.65 17.4 26.01 15.34 47.88 21.85

Bootstrap p-values are reported in parentheses. For bootstrapping 10,000 replications have been used. Boldface

figures indicate rejection of the relevant null hypothesis at the 5% level or higher. MW is Maddala and Wu v2 test

for a unit root in the model. The critical value for MW test is 37.57 at the 1% statistical level. A boldface figure

indicates non-rejection of a unit root.

Table 6

Fully modified OLS estimates (dependent variable is output, y)

Country Finance depth ( F ) Investment share (S ) Inflation ( p)

Colombia 3.21*** [3.00] À 0.01 [0.07] À 0.01 [1.19]

Paraguay 51.50*** [4.33] 0.56* [1.75] À 0.02 [0.36]

Peru 40.32*** [3.14] À 0.74 [0.87] À 0.01*** [2.87]

Mexico 3.08 [1.62] 0.82*** [2.70] 0.03 [0.53]

Ecuador 18.55 [1.50] 0.67*** [2.56] À 0.004 [0.01]

Honduras 30.40*** [3.76] 0.28 [1.15] 0.02 [1.09]

Kenya 36.55*** [3.72] 3.13*** [4.21] À 0.07 [1.07]

Thailand 83.11* [1.68] 3.05*** [2.96] 0.008 [0.58]

Dominican Republic 25.40*** [3.28] À 0.02 [0.49] À 0.03 [0.50]

Jamaica 39.17*** [3.83] 0.56 [0.56]

À0.07 [0.25]

Panel 14.13*** [2.57] 0.20* [1.91] À 0.04 [1.28]

Figures in brackets are t-statistics. (***) and (*) indicate statistical significance at the 1% and 10% level,

respectively.




on output but the relation does not seem to be statistically significant in Mexico and

Ecuador. The t -statistics are 1.62 and 1.50, respectively, so statistical significance is

marginal. Investment’s share and inflation are only exceptionally statistically significant.

Under the assumption of parameter homogeneity, that we may have to accept in view of the short span, the panel results should be more reliable. These results leave little ground

for assuming that financial depth has no impact on output. On the contrary, we get a clear

positive relation for the panel as a whole, as well as on a per country basis.

Our results are in line with King and Levine (1993a,b), Levine et al. (2000), Beck et al.

(2000), Levine (1999) and Khan and Senhadji (2000) who find positive effects of financial

depth on growth. The results contradict the time series evidence in Demetriades and Hussein

(1996), as well as Luintel and Khan (1999) who find bi-directional causality. Taking account

of the fact that panel unit root test and cointegration tests utilize the data in a more efficient

way, the panel results (as well as the majority of time series based tests) provide clear

evidence that there is a fairly strong long run relationship between financial depth and

output, that a long run causal relationship running from output to financial depth in unlikely

(since the equation with financial depth (F) as the dependent variable does not show

cointegration) and, therefore, the causal relationship runs from financial depth to output .

Another important issue is whether causality between output and financial deepening is

short run as well. To investigate this issue, we have specified error correction models

(ECM) of the form

D yt ¼ c þXm

i¼1

biD F t Ài þXm

i¼1

D xt À1 V g i þ kð yt À1 À xt À1 V d À d0 F t À1Þ þ v t ð15Þ

where yt À 1 À x t À 1 V d À d0 F t À 1 represents the equilibrium error, that is the deviation from

the long run relationship. The first important issue we consider is whether k p 0. If this is

not the case, the cointegration finding would not be reliable. The second important issue is

whether H 0: bi = 0 (all i = 1,. . .,m) can be rejected. If it can be rejected, there is no evidence

of short run causality. The v2 tests of short run causality as well as diagnostic statistics

(normality, autocorrelation and functional form misspecification) for the VEC model are

depicted in Tables 7 and 8. According to these results, the VEC model seems to be data

congruent and free from specification error for all countries we examine. The hypothesis of

short run causality can be rejected for all countries with the exception of the Dominican

Republic. However, the p-value of the test in this case is 0.03 suggesting that the

hypothesis can be rejected at the 5% level but not at 1%. Therefore, not even in this

case we have a definite result. Moreover, estimates of the speed of adjustment, k, have p-

values consistent with statistical significance, which leaves little doubt that the estimated

long run relationships are indeed structural. The same conclusions are supported for the

panel as a whole based on the Fisher test that aggregates the individual p-values.

Additionally, we estimate a VEC model allowing for panel data. The formulation is as

follows.

D yit ¼ ci þXm

l ¼1bl D yi;t Àl þXm

l ¼1D xi;t Àl

Vg l þ kð yi;t À1 À xi;t À1

Vd À d0 F i;t À1Þ þ v it

ð16Þ




where ci represents fixed country effects. This model can be estimated using instrumental

variables. Since this is a dynamic panel data model, it is well known that standard

estimation techniques like LSDV yield biased and inconsistent estimators in the panel data

case. For this reason, we must use an instrumental variables estimator to deal with the

correlation between the error term and lagged dependent variables D yi,t À 1. We have found

that m = 2 is necessary to satisfy the classical assumptions on the error term, so we use

D yi,t À 3 and D yi,t À 4 as instruments for the lagged dependent variables.

Estimates as well as diagnostic statistics for the VEC model are presented in Table 9.

Again the VEC model seems to be data congruent and free from specification error for all

Table 7

Short run causality tests between Output ( y) and Finance Depth ( F ): Error Correction Models (ECM)

Country Lags of financial

deepening, v2

p-values of speed

of adjustment, kColombia 2.29 [0.32] [0.07]

Paraguay 2.04 [0.36] [0.02]

Peru 4.76 [0.09] [0.02]

Mexico 0.54 [0.76] [0.06]

Ecuador 0.47 [0.78] [0.06]

Honduras 4.09 [0.12] [0.01]

Kenya 1.13 [0.57] [0.05]

Thailand 2.82 [0.24] [0.03]

Dominican Republic 7.08 [0.03] [0.02]

Jamaica 1.20 [0.74] [0.03]

Panel Fisher test 22.60 67.07

Figures in brackets represent asymptotic p-values associated with the tests. Fisher’s test is computed based on

p-values from individual tests. The critical value for Fisher test is 37.57 at the 1% statistical level. Boldface values

indicate statistical significance at the level 7% or higher.

Table 8

Diagnostic tests for the Vector Error Correction (VEC) model

Countries Jarque-Bera

Test (JB)

Lagrange Multiplier

Test ( LM 2)

Ramsey

Specification Test

Colombia 0.47 [0.79] 1.82 [0.39] 2.93 [0.08]

Paraguay 1.06 [0.59] 1.95 [0.37] 1.36 [0.50]Peru 0.63 [0.73] 5.56 [0.06] 0.01 [0.99]

Mexico 2.29 [0.12] 1.35 [0.51] 2.19 [0.33]

Ecuador 5.82 [0.06] 2.17 [0.34] 4.59 [0.10]

Honduras 0.46 [0.78] 1.41 [0.49] 0.36 [0.83]

Kenya 0.63 [0.73] 4.91 [0.08] 3.60 [0.13]

Thailand 0.84 [0.35] 2.58 [0.27] 1.76 [0.47]

Dominican Republic 1.85 [0.39] 4.26 [0.09] 2.93 [0.23]

Jamaica 0.33 [0.84] 1.12 [0.52] 2.63 [0.26]

Panel Fisher test 17.48 28.10 27.19

Figures in brackets represent asymptotic p-values associated with the tests. Jarque-Bera (JB) denotes the Jarque-

Bera normality Test of errors. Lagrange Multiplier Test ( LM ) tests the null hypothesis that there is no second order

autocorrelation. The Ramsey Test tests the null hypothesis that there is no functional form misspecification . The

critical value for Fisher test is 37.57 at the 1% statistical level. Boldface values indicate statistical significance at

the level 7% or higher.




countries we examine. The v2-test for the hypothesis that lags of financial development do

not contribute to output is not rejected, therefore there is no evidence of short run causality.

The most important implication of our findings is a policy recommendation: If policy

makers want to promote growth, then attention should be focused on long run policies, for example the creation of modern financial institutions, in the banking sector and stock

markets. From that point of view, our findings conform to earlier findings of empirical

studies that report routinely statistically significant coefficients of financial proxy variables

on output growth, for example Gelb (1989), Ghani (1992), King and Levine (1993a,b),

Levine and Zervos (1996) and Beck et al. (2000). These findings, as well as the findings in

the present study, stand against empirical evidence in Ireland (1994) and Demetriades and

Hussein (1996) that are consistent with the view that financial deepening is an outcome of

the growth process. Not only the evidence on cointegration and the statistical significance

of financial development is quite strong but the same pattern is confirmed by panel-based

tests as well. This is particularly important because although time series tests allow the possibility to examine causality contrary to cross-country regressions, their power could be

low given typical small sample sizes. Panel cointegration tests combine the ability of time

series studies to yield causality inferences with the increase in sample size afforded by

using cross-sectional data.

One notable implication of our findings is that results are not dramatically country-

specific (as in Demetriades and Hussein, 1996 for example). This offers a justification for

using panel-based unit root and cointegration tests. Another important implication of the

absence of short run causality, and the strong nature of long run causality, is the one

emphasized by Darrat (1999), namely that since the effect of financial development on

growth is realized in the short run, policy makers may be deceived to believe that there isno effect at all. The long run nature of the effect, however, is a necessary implication of the

fact that financial markets affect the cost of external finance to the firm and, therefore, their

Table 9

Panel vector error correction model

Variable Estimate

D yt À 1 0.17 [0.003]D yt À 2 0.09 [0.08]

D F t À 1 2.05 [0.99]

D F t À 2 3.60 [0.84]

DS t À 1 1.29 [0.63]

DS t À 2 2.26 [0.56]

D pt À 1 0.002 [0.18]

D pt À 2 0.0003 [0.86]

Error Correction Term ( ECT t À 1) À 0.32 [0.01]

Jarque -Bera Test (JB) 0.30 [0.87]

Likelihood Ratio Test LR(2) 1.45 [0.46]

Ramsey Test 4.06 [0.13]

Figures in brackets represent asymptotic p-values associated with the tests. Jarque-Bera (JB) denotes the Jarque-

Bera normality Test of errors. The Likelihood ratio Test (LR) tests for the null hypothesis that there is no second

order autocorrelation. The Ramsey Test tests the null hypothesis that there is no functional form misspecification.

Boldface figures denote statistical significance at the 1% level. Fixed effect estimates are not reported but are

available from the authors upon request.




effect materializes through facilitating the investment process itself. Unless conditions for

low-cost investment are created, long run growth is impossible.

5. Concluding remarks

In this paper we have combined cross-sectional and time series data to examine the

relationship between financial development and growth in ten developing countries.

Previous studies have used either cross-sectional or time series data but both approaches

have drawbacks. Using cross-sectional data leaves open the question of spurious

correlation arising from non-stationarity, and does not permit an examination of the

direction of causality. Using time series data, may yield unreliable results due to short time

spans of typical data sets. We have made use of panel unit root tests, and panel

cointegration analysis to conclude that there is fairly strong evidence in favor of the

hypothesis that long run causality runs from financial development to growth, that the

relationship is significant, and that there is no evidence of bi-directional causality. We have

used fully modified OLS to estimate the cointegrating relation, a method that deals with

the problem of endogeneity of regressors. Time series evidence is also supportive to the

idea that there exists a unique cointegrating vector between growth, financial development

and ancillary variables (investment share and inflation). The empirical evidence also points

to the direction that there is no short run causality between financial deepening and output,

so the effect is necessarily long run in nature. The important policy implication is that

policies aiming at improving financial markets will have a delayed effect on growth, but this effect is significant.

Acknowledgements

We wish to thank an anonymous referee for useful comments in a previous version of

this paper. Thanks also go to Peter Pedroni for providing his code to implement fully

modified estimation.

References

Arestis, P., Demetriades, P., 1997. Financial development and economic growth: assessing the evidence. Eco-

nomic Journal 107, 783–799.

Atje, R., Jovanovic, B., 1993. Stock market and development. European Economic Review 37, 623–640.

Beck, T., Levine, R., Loyaza, N., 2000. Finance and the sources of growth. Journal of Financial Economics 58,

261–300.

Bencivenga, V., Smith, B., Starr, R., 1995. Transaction costs, technological choice and endogenous growth.

Journal of Economic Theory 67, 153–177.

Benhabib, J., Spiegel, M.M., 2000. The role of financial development in growth and investment. Journal of

Economic Growth 5, 341–360.

Berthelemy, J., Varoudakis, A., 1996. Economic growth, convergence clubs, and the role of financial develop-

ment. Oxford Economic Papers 48, 300–328.

Breitung, L., 1999. The Local Power of Some Unit Root Tests for Panel Data, Discussion Paper. Humboldt

University, Berlin.




Caner, M., Hansen, B.E., 2001. Threshold Autoregression with a Unit Root. Econometrica 69, 1565–1596.

Chandavarkar, A., 1992. Of finance and development: neglected and unsettled questions. World Development 22,

133–142.

Darrat, A., 1999. Are financial deepening and economic growth causally related? Another look at the evidence.

International Economic Journal 13, 19–35.

Deidda, L., Fatouh, B., 2002. Non-linearity between finance and growth. Economics Letters 74, 339–345.

De Mello, J., 1999. Foreign direct investment-led growth: evidence from time series and panel data. Oxford

Economic Papers 51, 577–594.

Demetriades, P.O., Hussein, A.K., 1996. Does financial development cause economic growth? Time series

evidence from 16 countries. Journal of Development Economics 51, 387–411.

Demirgucß-Kunt, A., Maksimovic, V., 1998. Law, finance, and firm growth. The Journal of Finance LIII,

2107–2137.

Enders, W., Granger, C.W.J., 1998. Unit root tests and asymmetric adjustment with an example using the term

structure of interest rates. Journal of Business of Economic and Statistics 16, 304–312.

Gelb, A.H., 1989. Financial policies, growth and efficiency. World Bank Working Paper, vol. 202. June.

Ghani, E., 1992. How financial markets affect long run growth: a cross-country study. World Bank WorkingPaper, vol. PPR 843. January.

Greenwood, J., Jovanovic, B., 1990. Financial development and economic development. Economic Development

and Cultural Change 15, 257–268.

Greenwood, J., Smith, B., 1997. Financial markets in development and the development of financial market.

Journal of Economic Dynamic and Control 21, 181–1456.

Harris, R.D.F., Tzavalis, E., 1999. Inference for unit roots in dynamic panels where the time dimension is fixed.

Journal of Econometrics 91, 201–226.

Im, S.K., Pesaran, H.M., Shin, Y., 1997. Testing for Unit Roots in Heterogeneous Panel. Department of Applied

Econometrics, University of Cambridge.

Ireland, P.N., 1994. Money and growth: an alternative approach. American Economic, 47–65.

Johansen, S., 1988. Statistical analysis of cointegration vectors. Journal of Economics Dynamic and Control 12,

231–254.Khan, S.M., Senhadji, A.S., 2000. Financial Development and Economic Growth: An Overview. IMF Working

Paper. International Monetary Fund, Washington.

King, R.G., Levine, R., 1993a. Finance and growth: Schumpeter might be right. Quarterly Journal of Economics

108, 717 – 737.

King, R.G., Levine, R., 1993b. Finance, entrepreneurship and growth. Journal of Monetary Economics 32,

30–71.

Kiviet, J.F., 1995. On bias, inconsistency and efficiency of various estimators in dynamic panel models. Journal

of Econometrics 68, 53–87.

Levin, A., Lin, C.F., 1993. Unit root tests in panel data: asymptotic and finite sample properties. Mimeo

(September).

Levine, R., 1991. Stock markets, growth, and tax policy. Journal of Finance XLVI, 1445–1465.

Levine, R., 1997. Financial development and economic growth: views and agenda. Journal of Economic Liter-ature 35, 688–726.

Levine, R., 1998. The legal enviroment, banks, and long-run economic growth. Journal of Money, Credit, and

Banking 30, 596–613.

Levine, R., 1999. Law, finance and economic growth. Journal of Financial Intermediation 8, 8–35.

Levine, R., Zervos, S., 1996. Stock market development and long-run growth. World Bank Economic Review 10,

323–339.

Levine, R., Loyaza, N., Beck, T., 2000. Financial intermediation and growth: causality and causes. Journal of

Monetary Economics 46, 31–71.

Lucas Jr., R.E., 1988. On the mechanics of economic development. Journal of Monetary Economics 22, 3–42.

Luintel, B.K., Khan, M., 1999. A quantitative re-assessment of the finance-growth nexus: evidence from a

multivariate VAR. Journal of Development Economics 60, 381– 405.

Maddala, G.S., Kim, I.-M., 1998. Unit Roots, Cointegration, and Structural Change. Cambridge Univ. Press,

Cambridge.




Maddala, G.S., Wu, S., 1999. A comparative study of unit root tests with panel data and a new simple test.

Oxford Bulletin of Economics and Statistics 61, 631–652.

Maddala, G.S., Wu, S., Liu, P., 1999. Do panel data rescue Purchasing Power Parity (PPP) theory? In: Krishna-

kumar, J., Ronchetti, E. (Eds.), Panel Data Econometrics: Future Directions. Elsevier.

McKinnon, R.I., 1973. Money and Capital in Economic Development. Brooking Institution, Washington, DC.

Neusser, K., Kugler, M., 1998. Manufacturing growth and financial development: evidence from OECD coun-

tries. The Review of Economics and Statistics, 638–646.

Obstfeld, M., 1994. Risk-taking, global diversification, and growth. American Economic Review 84, 10–29.

O’Connell, P.G.J., 1998. The overvaluation of purchasing power parity. Journal of International Economics 44,

1–19.

Pagano, M., 1993. Financial markets and growth: an overview. European Economic Review 37, 613– 622.

Pedroni, P., 2000. Fully modified OLS for heterogeneous cointegrated panelsNon-Stationary Panels, Panel

Cointegration and Dynamic Panels, vol. 15. Elsevier, pp. 93–130.

Phillips, P.C.B., Hansen, B.E., 1990. Statistical inference in individual variables regression with I(1) process.

Review of Economic Studies 57, 99–125.

Pierse, R.G., Shell, A.J., 1995. Temporal aggregation and the power of tests for unit root. Journal of Econo-metrics 65, 335–345.

Robinson, J., 1952. The Rate of Interests and Other Essays. Macmillan, London.

Shaw, E.S., 1973. Financial Deepening in Economic Growth. Oxford Univ. Press, NY.

Singh, A., 1997. Financial liberalization, stock markets and economic development. Economic Journal 107,

771–782.


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