Journal of Development Economics 73 (2004) 55 – 74
www.elsevier.com/locate/econbase
Financial development and economic growth:
evidence from panel unit root and cointegration tests
Dimitris K. Christopoulos a,*, 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 data
estimation 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 imperfections. For an extensive survey of this literature, see Levine (1997). Among these factors
* Corresponding author. Tel.: +30-210-9224948; fax: +30-210-9229312.
E-mail address: christod@panteion.gr (D.K. Christopoulos).
0304-3878/$ - see front matter D 2003 Elsevier B.V. All rights reserved.
doi:10.1016/j.jdeveco.2003.03.002
56
D.K. Christopoulos, E.G. Tsionas / 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 directed
credit programs) hinder financial 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 investment.
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 very
badly 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 development 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 (organized
around 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) 55–74
57
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 activity
and 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 methodology
allows 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 only
for 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
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D.K. Christopoulos, E.G. Tsionas / Journal of Development Economics 73 (2004) 55–74
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 development 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, as
well 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 information 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 macroeconomic 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 many
difficulties, have not been without merit in several countries, not least in the highly
successful East Asian economies’’ (pp. 779 – 780).
D.K. Christopoulos, E.G. Tsionas / Journal of Development Economics 73 (2004) 55–74
59
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 manufacturing 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 manufacturing 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 as
supportive 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
60
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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 productivity. 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, Demirgücß-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 the
coefficients 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 by
using 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.
D.K. Christopoulos, E.G. Tsionas / Journal of Development Economics 73 (2004) 55–74
61
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 by
appropriate 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 Fit þ b2i Sit þ b3i ṗit þ uit
ð1Þ
where yit is real output in country i and year t, Fit is a measure of financial depth, Sit is the
output share of investment, ṗit is inflation, and uit is an error term. Since the direction of
causality is not clear we also specify the model
Fit ¼ b0i þ b1i yit þ b2i Sit þ b3i ṗit þ vit
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 the
dependent 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 cointegration 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
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D.K. Christopoulos, E.G. Tsionas / Journal of Development Economics 73 (2004) 55–74
GDP and the share of investment, S, is the share of gross fixed capital formation to
nominal GDP. Inflation rate (ṗ ) 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. Regarding 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 (ti)
according to
pffiffiffiffi
N ðt̄ E½ti j qi ¼ 0Þ
tIPS ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
! N ð0; 1Þ
ð3Þ
var½ti j qi ¼ 0
P
where t̄ ¼ N 1 Ni¼1 ti . The moments of E[tijqi = 0] and var[tijqi = 0] are obtained by
Monte Carlo simulation and are tabulated in
PIPS.
The MW statistic is given by P ¼ 2 Ni¼1 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. 2N, 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 on
different 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 ṗ. 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 from
time series as well as cross-section data once again. In this context three panel
cointegration tests are conducted.
D.K. Christopoulos, E.G. Tsionas / Journal of Development Economics 73 (2004) 55–74
63
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;t1 þ zitVc þ uit
ð4Þ
where zit are deterministic variables, uit is iid(0,r2) and qi = q. The test statistic is a tstatistic on q given by
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
N X
T
X
2
ðq̂ 1Þ
ỹi;t1
i¼1 t¼1
tq ¼
ð5Þ
se
where
ỹit ¼ yit T
X
ũit ¼ uit hðt; sÞyis ;
s¼1
s2e ¼ ðNT Þ1
T
X
hðt; sÞuis
hðt; sÞ ¼ ztV
s¼1
N X
T
X
T
X
!
zt ztV zs ;
t¼1
ũ2it ;
i¼1 t¼1
and q̂ is the OLS estimate of q. It can be shown that if there are only fixed effects in the
model, then
pffiffiffiffi
pffiffiffiffi
51
N T ðq̂ 1Þ þ 3 N ! N 0;
ð6Þ
5
and if there are fixed effects and a time trend,
pffiffiffiffi
2895
N ðT ðq̂ 1Þ þ 7:5Þ ! N 0;
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
!
pffiffiffiffi
3
3ð17T 2 20T þ 17Þ
N q̂ 1 þ
! N 0;
ð8Þ
T þ1
5ðT 1ÞðT þ 1Þ3
If there are fixed effects and a time trend, then
pffiffiffiffi
N q̂ 1 þ
15
2ðT þ 2Þ
! N 0;
15ð193T 2 728T þ 1147Þ
112ðT þ 2Þ3 ðT 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
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D.K. Christopoulos, E.G. Tsionas / Journal of Development Economics 73 (2004) 55–74
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 pP
i denotes the p-value of the Johansen
statistic for the ith unit, then we have the result 2 Ni¼1 log pi fv22N . 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
Dyt ¼ h1Vxt1 1ðzt1 VkÞ þ h2Vxt1 1ðzt1 zkÞ þ ut
ð10Þ
where yt is the series we consider, k is the threshold parameter, xt 1=( yt 1,rtV 1 ,
Dyt 1,. . .,Dyt k)V, zt u yt yt m for some m z 1 (m = 1 in this application), and
rt 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) P
is estimated by OLS for a fixed ka[k,k̄], and the residual variance
s2 ðkÞ ¼ T 1 Tt¼1 ûðkÞ2 is computed.
The threshold parameter is estimated by k̂ ¼ agrmin: s2 ðkÞk. For the estimate k̂, the
residuals ût and the residual variance s2 are computed. To test for a threshold effect, we
need to test the hypothesis H0: h1 = h2 which can be tested using a Wald test W = T(s02/
s2 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 pvalues 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-sided
formulation of Caner and Hansen (2001), namely H0: q1 = q2 = 0 versus the alternative H1:
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 = t12 + t22 where ti signifies the t-ratios for q̂i 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)
P tests can be constructed by considering an MW formulation of the form
2 Ni¼1 log pi fv22N, 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
D.K. Christopoulos, E.G. Tsionas / Journal of Development Economics 73 (2004) 55–74
65
proceed to estimate the Eq. (1) by the method of fully modified OLS appropriate for
heterogeneous cointegrated panels (Pedroni, 2000). This methodology addresses the
problem of non-stationary regressors, as well as the problem of simultaneity bias raised
by Levine et al. (2000): OLS estimation yields biased results because, in general, the
regressors are endogenously determined in the I(1) case. We consider the following
cointegrated system for panel data
yit ¼ ai þ xitVb þ uit
ð11Þ
xit ¼ xi;t1 þ eit
ð12Þ
where nit=[uit,eitV] 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
!1
!
N
T
N
T
X
X
X
X
2
2
1 1
b̂FM b ¼
ðxit x̄t Þ
ðxit x̄t Þu*it T ĉi
X̂22i
X̂11i X̂22i
i¼1
t¼1
i¼1
t¼1
ð13Þ
ûit * ¼ uit X̂1
22i X̂21i ;
0
ĉi ¼ Ĉ21i þ X̂021i X̂1
22i X̂21i ðĈ22i þ X̂22i Þ
ð14Þ
where the covariance matrix can be decomposed as Xi = Xi0 + Ci + Ci where Xi0 is the
contemporaneous covariance matrix, and Ci is a weighted sum of autocovariances. Also,
X̂i0 denotes an appropriate estimator of Xi0.
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 and
Jamaica, 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 hypothesis
of 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
66
D.K. Christopoulos, E.G. Tsionas / Journal of Development Economics 73 (2004) 55–74
Table 1
ADF unit root tests
Country
Output ( y)
Levels
Colombia
0.90
Paraguay
1.84
Peru
2.54
Mexico
2.19
Ecuador
4.94***
Honduras
2.10
Kenya
0.63
Thailand
2.57
Dominican Republic 1.29
Jamaica
3.24*
Finance depth ( F)
Investment share (S) Inflation (ṗ)
Diff
Levels
Diff
Levels
Diff
Levels
Diff
1.86
3.26*
3.72**
3.15*
3.51**
3.88**
5.85***
2.56
2.89
3.32*
0.36
1.34
2.31
2.21
1.38
1.75
1.20
3.99***
1.33
2.12
3.31*
3.65**
3.72**
5.32***
5.21***
2.19
3.78**
2.97
5.83***
3.78**
3.35*
3.69**
3.22*
2.47
2.08
1.14
2.12
2.01
3.39*
2.53
3.30*
2.84
5.20***
3.99**
3.83**
3.99*
2.64
2.63
4.71***
2.96
2.98
1.93
2.63
1.49
2.28
2.59
2.13
4.04**
1.71
3.05
3.29*
4.95***
4.07**
3.68**
3.28*
4.17**
3.41**
3.98**
3.46**
4.32***
Levels and Diff denote the augmented Dickey-Fuller t-tests for a unit root in levels and first differences
respectively. 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.
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
Output ( y)
Finance depth ( F)
Investment share (S)
Inflation (ṗ)
Levels
First differences
IPS
MW
IPS
MW
0.18
2.71
0.04
0.47
27.12
14.77
30.37
26.37
4.52***
6.63***
5.81***
5.19***
58.33***
83.64***
62.98***
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.
D.K. Christopoulos, E.G. Tsionas / Journal of Development Economics 73 (2004) 55–74
67
Table 3
Johansen Cointegration tests
Country
Max eigenvalue statistic H0: rank = r
Colombia
Paraguay
Peru
Mexico
Ecuador
Honduras
Kenya
Thailand
Dominican Republic
Jamaica
r = 0 (62.99)
r V 1 (42.44)
r V 2 (25.32)
r V 3 (12.25)
65.58a
115.12a
106.88a
117.87a
125.14a
141.06a
98.83a
66.27a
141.83a
97.14a
25.83
22.44
33.83
26.64
25.39
38.27
29.02
36.99
40.86
36.39
8.02
11.86
13.74
13.52
9.16
14.21
14.47
18.61
17.32
14.25
0.25
5.47
4.83
5.84
3.45
5.46
6.11
5.65
6.50
6.51
r denotes the number of cointegrating vectors. The optimal lag lengths for the VARs were selected by minimising
the Schwarz criterion. Numbers in parentheses next to r = 0, r V 1, r V 2 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.
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 t1 statistic has a p-value equal to 0.003 (the test value is 5.04) but the
t2 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, t1 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 cointegration in the case of t1 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 t1 and t2
Table 4
Panel cointegration tests
Levin-Lin
F.E.
Fisher v2 cointegration test
Harris-Tzavalis
F.E.T.
F.E.
F.E.T.
Dependent variable: output (y)
8.36***
0.89
77.13***
5.57***
Dependent variable: financial depth (F)
1.20
0.50
0.85
1.65
r=0
76.09
rV1
30.73
rV2
28.91
rV3
23.26
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.
68
D.K. Christopoulos, E.G. Tsionas / Journal of Development Economics 73 (2004) 55–74
Table 5
Testing for threshold unit roots in the error correction term (Ut)
Country
Wald test for
Wald test for threshold effect in
threshold effect Intercept
Ut 1
DUt 1
t-test for stationarity
Colombia
Paraguay
Peru
Mexico
Ecuador
Honduras
Kenya
Thailand
Domin.
Republic
Jamaica
MW panel
test
7.12
16.0
19.7
9.22
8.29
3.97
8.18
16.0
8.64
4.02
2.82
0.52
5.04
1.83
3.30
2.38
3.25
2.83
(0.504)
(0.074)
(0.043)
(0.302)
(0.408)
(0.901)
(0.395)
(0.054)
(0.343)
7.63 (0.457)
28.65
t1
0.078 (0.950) 5.50 (0.140) 0.018 (0.940)
10.1 (0.075)
0.898 (0.598) 10.1 (0.042)
1.78 (0.743)
7.61 (0.084) 1.21 (0.544)
2.26 (0.655)
5.45 (0.128) 1.81 (0.429)
4.54 (0.376) 0.435 (0.723) 1.77 (0.456)
1.08 (0.799)
2.22 (0.387) 0.023 (0.931)
6.77 (0.158)
2.95 (0.314) 1.04 (0.558)
6.17 (0.215) 0.470 (0.708) 0.009 (0.956)
0.015 (0.982) 6.84 (0.069) 0.335 (0.740)
3.13 (0.553)
17.4
t2
(0.030)
(0.131)
(0.844)
(0.003)
(0.305)
(0.052)
(0.168)
(0.052)
(0.093)
0.965 (0.582) 3.00 (0.301) 1.58 (0.39)
26.01
15.34
47.88
0.236
3.80
2.85
0.323
1.26
0.456
2.45
1.90
1.55
(0.755)
(0.030)
(0.111)
(0.758)
(0.503)
(0.692)
(0.152)
(0.340)
(0.974)
0.997 (0.539)
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.
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 ( 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 6
Fully modified OLS estimates (dependent variable is output, y)
Country
Finance depth ( F)
Investment share (S)
Inflation (ṗ)
Colombia
Paraguay
Peru
Mexico
Ecuador
Honduras
Kenya
Thailand
Dominican Republic
Jamaica
Panel
3.21*** [3.00]
51.50*** [4.33]
40.32*** [3.14]
3.08 [1.62]
18.55 [1.50]
30.40*** [3.76]
36.55*** [3.72]
83.11* [1.68]
25.40*** [3.28]
39.17*** [3.83]
14.13*** [2.57]
0.01
0.56*
0.74
0.82***
0.67***
0.28
3.13***
3.05***
0.02
0.56
0.20*
0.01
0.02
0.01***
0.03
0.004
0.02
0.07
0.008
0.03
0.07
0.04
[0.07]
[1.75]
[0.87]
[2.70]
[2.56]
[1.15]
[4.21]
[2.96]
[0.49]
[0.56]
[1.91]
[1.19]
[0.36]
[2.87]
[0.53]
[0.01]
[1.09]
[1.07]
[0.58]
[0.50]
[0.25]
[1.28]
Figures in brackets are t-statistics. (***) and (*) indicate statistical significance at the 1% and 10% level,
respectively.
D.K. Christopoulos, E.G. Tsionas / Journal of Development Economics 73 (2004) 55–74
69
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
Dyt ¼ c þ
m
X
bi DFti þ
i¼1
m
X
Dxt1
V g i þ kðyt1 xt1
V d d0 Ft1 Þ þ vt
ð15Þ
i¼1
where yt 1 xtV 1 d d0Ft 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 H0: 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 pvalues 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.
Dyit ¼ ci þ
m
X
l¼1
bl Dyi;tl þ
m
X
Dxi;tl
V g l þ kðyi;t1 xi;t1
V d d0 Fi;t1 Þ þ vit
l¼1
ð16Þ
70
D.K. Christopoulos, E.G. Tsionas / Journal of Development Economics 73 (2004) 55–74
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, k
Colombia
Paraguay
Peru
Mexico
Ecuador
Honduras
Kenya
Thailand
Dominican Republic
Jamaica
Panel Fisher test
2.29 [0.32]
2.04 [0.36]
4.76 [0.09]
0.54 [0.76]
0.47 [0.78]
4.09 [0.12]
1.13 [0.57]
2.82 [0.24]
7.08 [0.03]
1.20 [0.74]
22.60
[0.07]
[0.02]
[0.02]
[0.06]
[0.06]
[0.01]
[0.05]
[0.03]
[0.02]
[0.03]
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.
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 Dyi,t 1. We have found
that m = 2 is necessary to satisfy the classical assumptions on the error term, so we use
Dyi,t 3 and Dyi,t 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 8
Diagnostic tests for the Vector Error Correction (VEC) model
Countries
Jarque-Bera
Test (JB)
Lagrange Multiplier
Test (LM2)
Ramsey
Specification Test
Colombia
Paraguay
Peru
Mexico
Ecuador
Honduras
Kenya
Thailand
Dominican Republic
Jamaica
Panel Fisher test
0.47 [0.79]
1.06 [0.59]
0.63 [0.73]
2.29 [0.12]
5.82 [0.06]
0.46 [0.78]
0.63 [0.73]
0.84 [0.35]
1.85 [0.39]
0.33 [0.84]
17.48
1.82 [0.39]
1.95 [0.37]
5.56 [0.06]
1.35 [0.51]
2.17 [0.34]
1.41 [0.49]
4.91 [0.08]
2.58 [0.27]
4.26 [0.09]
1.12 [0.52]
28.10
2.93 [0.08]
1.36 [0.50]
0.01 [0.99]
2.19 [0.33]
4.59 [0.10]
0.36 [0.83]
3.60 [0.13]
1.76 [0.47]
2.93 [0.23]
2.63 [0.26]
27.19
Figures in brackets represent asymptotic p-values associated with the tests. Jarque-Bera (JB) denotes the JarqueBera 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.
D.K. Christopoulos, E.G. Tsionas / Journal of Development Economics 73 (2004) 55–74
71
Table 9
Panel vector error correction model
Variable
Estimate
Dyt 1
Dyt 2
DFt 1
DFt 2
DSt 1
DSt 2
Dṗt 1
Dṗt 2
Error Correction Term (ECTt 1)
Jarque -Bera Test (JB)
Likelihood Ratio Test LR(2)
Ramsey Test
0.17
0.09
2.05
3.60
1.29
2.26
0.002
0.0003
0.32
0.30
1.45
4.06
[0.003]
[0.08]
[0.99]
[0.84]
[0.63]
[0.56]
[0.18]
[0.86]
[0.01]
[0.87]
[0.46]
[0.13]
Figures in brackets represent asymptotic p-values associated with the tests. Jarque-Bera (JB) denotes the JarqueBera 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.
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 countryspecific (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 is
no 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
72
D.K. Christopoulos, E.G. Tsionas / Journal of Development Economics 73 (2004) 55–74
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.
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