Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
The Long-run Relationship between the Financial System
and Economic Growth: New Evidence from Iran
Abbas Alavi Rad* and Fatemeh Etemadmoghaddam**
In this paper, we examine the impact of financial system on economic growth
in Iran; with consider other variables affecting the economic growth, such as
inflation, trade openness and gross fixed capital formation. So, an attempt has
been made to estimate long-run economic growth elasticity to financial system
factors by applying Autoregressive Distributed Lag (ARDL) and Dynamic
Ordinary Least Squares (DOLS) procedures from 2000:1 to 2011:4. According
to the theories and previous empirical studies, financial system factors such as
banking sector credit available to the private sector, market capitalisation and
stocks traded have positive significant effect on economic growth in Iranian
economy.
JEL Codes: G21; O43; C22
1. Introduction
Empirical investigations about the relationship between financial sector development and
economic growth began to appear with Goldsmith (1969). However, the theoretical
literature dating back to Bagehot (1873) and Schumpeter (1911) and later broadened by
Hicks (1969), McKinnon (1973), and Shaw (1973), highlights the importance of financial
intermediation in facilitating economic activity.
The literature on relationship between financial sector development and economic growth
can be divided into three branches. One strand of this literature examines the impact of
stock market developments, namely, market capitalisation, turnover ratio, and stocks
traded on economic growth (see Bekaert, Harvey, and Lundblad, 2001; Shen and Lee,
2006). The second strand of this literature focuses on the relationship between banking
sector developments, namely, private credit and liquid liabilities, and economic growth
(see Amable and Chatelain 2001; Cole, Moshirian, and Wu, 2008). Finally, part of
literature examines the effect of stock market and banking sector (commonly referred to
as the financial system in the literature) developments on economic growth (see Seetanah
et al., 2010; Narayan and Narayan, 2013).
This study contributes to this literature by examining the relationship between financial
and banking sector developments and economic growth for Iran. Our study is different
from the previous studies in Iran in two ways. We, for the first time in previous literature in
Iran, examine the financial system (stock market/banking sector developments) on
economic growth of Iran.
Hoshmand and Daneshnia (2011) examined only effect of banking sector development on
economic growth considering other effective factors on economic growth of Iran.
Therefore, we extend the Hoshmand and Daneshnia (2011) study by considering the
effect of stock market and banking sector developments on economic growth of Iran. A
* Department of Economics, Abarkouh Branch, Islamic Azad University, Abarkouh, Iran.
Email: alavi_rad@abarkouhiau.ac.ir
** Department of Economic, Science and Research Branch, Islamic Azad University, Yazd, Iran.
Email: femoghaddam@yahoo.com
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Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
second way our study is different is as follows. We examine the impact of stock market
and banking sector developments indicators on economic growth of Iran with two
procedures - the autoregressive distributed lag (ARDL), and dynamic ordinary least
squares (DOLS) – to compare model sensitivity based on different estimators.
The rest of the paper is as follows: Section 2 reviews existing literature on the link
between financial sector and economic growth; Section 3 describes the methodology
applied in this research as well as sources of data; section 4 deals with the empirical
analysis and section 5 concludes the study.
2. Literature Review
Theories give contradictory predictions about the incidence of financial system
development on economic growth, and about the separate impact of banking sector on
growth and stock market on growth (see Ben Naceur and Ghazouani, 2007). Boyd and
Prescott (1986) argue that banks ease information frictions and therefore resource
allocation while Stiglitz (1985) and Bhide (1993) defend the idea that banks are more
efficient than equity markets in improving resource allocation and corporate governance.
However, some models stress that competitive stock markets reduce the
counterproductive monopoly power of banks and boost innovation projects (see Allen and
Gale, 2000). Finally, some theories argue that banking sector and stock market contribute
together to economic growth by improving information dissemination and reducing
transaction costs.
2.1. The Stock Market Developments-Growth Puzzle
Among literatures on the role of financial sector in the growth process stock market
development has received recently considerable attention. Pioneering work from Spears
(1991), Pardy (1992) Atje and Jovanovic (1993), show that stock market development is
strongly correlated with growth rates of real GDP per capita. More importantly, they found
that stock market liquidity predict the future growth rate of economy. Demirg¨u¸c-Kunt and
Levine (1996), Demirg¨u¸c-Kunt and Maksimovic (1996) examine the effect of stock
market development on economic growth. Filer, Hanousek, and Campos (1999),
Rousseau and Wachtel (2000) examined stock market‐growth nexus and exhibited
positive casual correlation between stock market development and economic activity.
Levine and Zervos (1996) use various indicators of stock market developments for a
sample of 41 countries over the period of 1976-1993, and show that there is a significant
relationship between measures of stock market developments and economic growth.
Van Nieuwerburgh, Buelens and Cuyvers (2006) investigate the long-run relationship
between financial market development and economic development in Belgium. They find
strong evidence that stock markets development caused economic growth in Belgium.
Adamopoulos (2010) investigates the causal relationship between stock market
development and economic growth for Germany. The results show that there is a
unidirectional causality between stock market development and economic growth with
direction from stock market development to economic growth.
Many researchers examined the relationship between stock market developments and
economic growth during the last decades. But, results are mixed. There exists some
authors who could not established any significant link between stock market development
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Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
and growth such as Bencivenga and Smith (1991), Ben Naceur and Ghazouani (2007)
and Adjasi and Biekpe (2006) who looked at developing countries (see Seetanah et al.,
2010).
2.2. The Banking Sector Developments-Growth Puzzle
Previous empirical studies have strongly supported the theoretical proposition that the
banking system is an essential determinant of a country‘s economic development (See
Levine, 2005). King and Levine (1993) study the impact of banking sector development on
growth prospects. Jayarathne and Strahan (1996) find that banking deregulation led to
higher regional economic growth within the U.S.
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.
Levine (1998) showed a positive and significant correlation between banking sector
development and long-run economic growth in their study of 47 countries. He also suggest
that the level of banking development measured as the ratio of bank loans to the private
sector to GDP is directly related with the level of economic growth. Levine, Loayza and
Beck (2000) measure the growth effect of the ―exogenous component‖ of banking
development. Rousseau and Xiao (2007) in their study of China found that banking sector
development was central to the Chinese success.
Cole, Moshirian, and Wu (2008) Using dynamic panel techniques to analyse panel data
from 18 developed and 18 emerging markets, they find a positive and significant
relationship between bank stock returns and future GDP growth that is independent of the
previously documented relationship between market index returns and economic growth.
Wolde-Rufael (2009) re-examines the causal relationship between banking sector
development and economic growth in Kenya for the period 1966-2005. He selects money
supply (M2), liquid liabilities (M3), domestic bank credit to the private sector and total
domestic credit provided by the banking sector and show that banking development
promotes economic growth in Kenya.
2.3. The Financial System-Growth Puzzle
Over the last two decades, the relationship between economic growth and the financial
system, whose components are stock markets and the banking system, has received
considerable attention (e.g. Levine, 1991; Capasso, 2008)
Levine and Zervos (1998) have focused on the relationship between economic growth and
financial system development using both banks and stock markets indicators. They
empirically assess the relationship between both stock markets and banks development
and economic growth for a sample of 42 countries over the period of 1976-1993.
Arestis, Demetriades and Luintel (2001) use quarterly data on five developed countries
and find that both banks and stock markets development lead to economic growth. They
also find that the impact of banking sector development is substantially larger than that of
stock markets development.
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Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
Beck and Levine (2004) examine a panel of 40 developed and developing countries over
the period 1976–1998, and estimated the impact of stock market and banking sector
developments on economic growth using the Arellano and Blundell system-GMM
estimator. They found that stock market and banking sector developments both have
statistically significant and positive effects on economic growth
Seetanah et al. (2010) focus on a panel set of developing countries with the purpose of
simultaneously examining banking sector development, stock market development, and
economic growth in a unified framework. Results from the analysis showed that stock
market development is an important ingredient of growth, but with a relative lower
magnitude as compared to the other determinants of growth, particularly with banking
development.
Narayan and Narayan (2013) examine the impact of the financial system on economic
growth for a panel of 65 developing countries. They find that for the full panel of 65
countries there is evidence of financial sector-led growth, bank credit has a negative effect
on economic growth.
3. Data and Model Specification
3.1. Data
We use quarterly data in exploring the relationship between financial system and
economic growth in Iran. Its information is according to the time series and duration of this
study is for the period 2000:1 to 2011:4. The following variables are used in the
estimation: economic growth, gross fixed capital formation (GFCF), inflation, trade
openness measured as exports plus imports as a percentage of GDP, market
capitalisation of listed companies as a percentage of GDP, domestic credit provided by
the banking sector as a percentage of GDP, and stocks traded as a percentage of GDP.
In our study, the banking sector is proxied by domestic credit provided by the banking
sector, consistent with the literature. All data are extracted from the time series data
published by the Central Bank of Iran (CBI).
3.2. Model Specification
The model that has been used in this research, is based on the principles of some earlier
studies (e.g. King and Levine, 1993; Pagano 1993; Demirgiic-Kunt and Levine 1996;
Rousseau and Wachtel, 2000; Levine and Zervos, 1998; Christopoulos and Tsionas,
2004; Seetanah et al., 2010; Narayan and Narayan, 2013).
The model takes the following functional form:
Yt   0  1 X t   2 Ft  U t
The dependent variable output, Y proxies by the economic growth. X is a vector of core
explanatory variables, namely, inflation, gross fixed capital formation, and trade openness
(exports plus imports divided by GDP) used to model economic growth.
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Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
F is our measure of stock market/banking sector development, proxied by stocks traded
as a percentage of GDP, market capitalisation as a percentage of GDP, and banking
sector credit available to the private sector as a percentage of GDP and U is the error
term.
The empirical analysis in this paper is performed in 3 steps. First, a number of alternative
tests are available for testing whether a series is stationary. Usually augmented Dickey
Fuller (ADF) and Phillips and Perron (1988) tests have been used by researchers. This
study used ADF test for finding unit roots in time series. An indication of whether the
researcher should supplement ADF tests by also using the adjustments proposed by
Phillips and Perron (1988) can be gained by inspection of the diagnostic statistics from the
ADF regression (Perman, 1991). Second, ARDL bound testing procedure is used to
establish a cointegrating (long-run equilibrium) relationship between output and financial
system proxies.
Third, 2 types of cointegration estimation techniques - the ARDL and DOLS - are utilized
to estimate the output-financial system regression equation. ARDL proposed by Pesaran,
Shin and Smith (2001) and DOLS of Stock and Watson (1993).The main advantage of
ARDL and DOLS is because of it flexibility, which is can be applied irrespective of whether
underlying regressors are purely I(0), purely I(1) or mutually cointegrated.
4. Empirical Results
In first step, we first conduct augmented Dickey Fuller (ADF) test to establish the order of
integration for the economic growth (Y), inflation (INF), measure of trade openness
(OPEN), gross fixed capital formation (GFCF), measure of banking development (CPS),
market capitalisation (MCC) and stocks traded (STT) series. The results of the unit root
tests are presented in Table 1. The null hypothesis of unit root is not rejected by ADF test
for all series and so are the series non-stationary in the level. We conducted the same test
on the first difference of all series and found them stationary. As a result, these data
series can be characterized as I(1) for period of analysis.
Table 1: Results of Unit Root Test
Series
Y
INF
OPEN
GFCF
CPS
MCC
STT
Order
Level
st
1 difference
Level
st
1 difference
Level
st
1 difference
Level
st
1 difference
Level
st
1 difference
Level
st
1 difference
Level
st
1 difference
1
ADF
-2.6131
-7.1471
-3.5297
-5.5843
-3.5155
-2.9281
-2.4710
-2.9471
-2.2091
-8.4770
-1.9413
-3.6379
-2.2645
-3.5956
1 Augmented Dickey-Fuller unit root test, denotes significance at 5%
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Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
Once all the series are non-stationary in the level, one can estimate an econometric model
only if they are cointegrated. It can be concluded from the above discussion that we can
use Johansen procedure to establish long-run relationship among variables. But, there are
some limitations that can affect the validity of the estimation results. With a quarterly data
comprising 48 observations, the Johansen tests can be subject to size and power bias.
The importance of applying a correction factor for the Johansen procedure in small
samples is now well known. The correction factor is necessary to reduce the excessive
tendency of the tests to falsely reject the null hypothesis of no cointegration often
associated with data of relatively short span.
A number of papers including Reimers (1992) and Cheung and Lai (1993) have
documented the importance of this correction factor for the small sample. Cheung and Lai
(1993) had provided the correction factor of Johansen likelihood ratio test while Reinsel
and Ahn (1992) suggested an adjustment to the estimated maximum eigenvalue and
Trace statistics. So, the small number of observations affects the validity of the estimation
results.
The ARDL model overcomes this problem by introducing bounds testing procedure to
establish long-run relationship among variables. Pesaran and Shin (1995) showed that
ARDL modeling for univariate cointegration test for small sample will be the most
appropriate.
Before, applying the ARDL bounds testing to examine cointegration between the
variables, we have to select the appropriate lag length for the computation of F-statistics.
Given the Quarterly data available for estimation, we set the maximum lag order of the
various variables in the model equal to four. In this study, the lag length criteria was
obtain from unrestricted VAR estimation results which based on the maximum value of
Schwarz Bayesian Criterion (SBC). Based on the VAR estimation, the maximum value of
SBC is equal to 4. Narayan (2005) pointed out that the critical bounds developed by
Pesaran, Shin and Smith (2001)) are not suitable for small sample. Our sample consists of
T = 48, we use critical bounds developed by Narayan (2005). The results of the ARDL
bounds testing approach to cointegration are reported in Table 2.
Table 2: ARDL Bound Test to Long-run Cointegration
Estimated Models
F(Y| INF, OPEN, GFCF, CPS)
F(Y| INF, OPEN, GFCF, MCC)
F(Y| INF, OPEN, GFCF, STT)
F–
Statistics
5.7120
6.0470
8.6782
Lag
order
4
Bound
Calculated
Value
I(0)
4.385
3.219
2.711
I(1)
5.615*
4.378**
3.823***
Diagnostic tests
2
R
0.96
0.97
0.98
Adj-R
0.94
0.95
0.97
2
D.W
2.05
1.75
2.28
Note: *, ** and *** represent significance at 1, 5 and 10% level respectively.
Our computed F-statistic exceeds upper critical bound at 1% significance level once
economic growth are used as predicted variable. This confirms the presence of
cointegration between the variables over the period of 2000:1–2011:4. This entails that
vector of core explanatory variables, namely, inflation (INF), trade openness (OPEN), and
gross fixed capital formation (GFCF), with consider each financial system variables
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Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
(banking development (CPS), market capitalisation (MCC) and stocks traded (STT) ) and
economic growth are cointegrated for long-run relationship in case of Iran.
The second stage for univariate cointegration test is to estimate the long-run coefficients
of model. Table 3 presents the solved static long-run results of three models with two
procedures: models 1-3 estimate by the ARDL and DOLS procedures.
In model 1 of Table 3, we estimate the impact of domestic credit provided by the banking
Sector on economic growth. We find that domestic credit provided by the banking sector
as a percentage of GDP have a statistically significant and positive effect on economic
growth only with DOLS procedure. The results suggest that trade openness has a
statistically significant and negative effect on economic growth, also inflation has a
statistically significant (at the 5% level) and negative effect on economic growth with two
procedures. Finally, we find that The GFCF has a statistically significant (at the 1% level)
and positive effect on economic growth with two procedures.
Table 3: Estimated Long-run Coefficients the ARDL and DOLS Procedures
Variable
INF
OPEN
GFCF
CPS
Model 1
ARDL
DOLS
a
-0.04657 -0.01655
(0.059)
(0.0000)
a
a
-1.5791
-2.94717
(0.000)
(0.0000)
a
a
0.00019
0.00023
(0.001)
(0.0000)
a
0.0004
0.19302
(0.985)
(0.0000)
Model 2
ARDL
DOLS
-0.00213
-0.08325
(0.943)
(0.2905)
a
-0.57913
-2.67626
(0.063)
(0.0069)
a
a
0.00012
0.00023
(0.004)
(0.0046)
0.01958
(0.168)
MCC
0.03441
(0.1035)
STT
C
-2.5399
(0.087)
-6.84028
(0.0000)
-5.7581
(0.040)
Model 3
ARDL
DOLS
a
-0.10327
-0.08339
(0.018)
(0.0006)
a
a
-1.0270
-1.24252
(0.002)
(0.0000)
a
a
0.00014
0.00018
(0.001)
(0.0000)
-6.15285
(0.0157)
0.07169
(0.124)
-3.3590
(0.047)
0.087
(0.0226)
-5.40781
(0.0050)
The p-values are reported to examine the null.
Denotes statistical significance at the 1% level.
a
Next, in model 2 of Table 3, we estimate the impact of market market capitalisation on
economic growth. We find that market capitalisation of listed companies as a percentage
of GDP have a statistically significant (at the 10% level) and positive effect on economic
growth with DOLS procedure. The results show that same to model 1, trade openness has
a statistically significant (at the 5% level) and negative effect on economic growth with two
procedures, while inflation has a statistically insignificant and negative effect on economic
growth with two procedures. In addition, we find that The GFCF has a statistically
significant (at the 1% level) and positive effect on economic growth with ARDL and DOLS
procedures.
Finally, in model 3 of Table 3, we estimate the impact of stocks traded on economic
growth. We find that stocks traded as a percentage of GDP have a statistically significant
(at the 5% level) and positive effect on economic growth only with DOLS procedure. In
addition, the GFCF has a statistically significant (at the 1% level) and positive effect on
economic growth with two procedures, while inflation has a statistically significant (at the
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Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
1% level) and negative effect on economic growth. Trade openness also has a statistically
significant (at the 1% level) and negative effect on economic growth with two procedures.
The estimated coefficients show that financial system variables (CPS, MCC and STT)
have a positive effect on economic growth in long-run, and all the regressors are
statistically significant at least with one procedure.
For the purpose of interpreting the coefficient estimates, it is evidently crucial that the
long-run parameter estimates be structurally stable over the sample period of estimation.
To test for structural stability of the parameter estimates, this study use a series of
parameter constancy tests for I(1) processes proposed by Hansen (1992).
The use of Hansen‘s parameter stability testing framework is instructive in the sense that it
permits testing for parameter stability as well as for cointegration. This is possible through
the use of Hansen‘s test statistic, Lc, which considers instability due to relatively constant
parameter variation over the sample period. Thus, this statistic tests the null of
cointegration against the alternative of no cointegration. The parameter instability test
results presented in Table 4.
Table 4: Hansen Parameter Instability Test
Models
1
2
3
Lc statistic
0.108565
0.050316
0.173775
Stochastic
Trends (m)
4
4
4
Deterministic
Trends (k)
0
0
0
Excluded
Trends (p2)
0
0
0
Prob.*
> 0.2
> 0.2
> 0.2
*Hansen (1992b) Lc(m2=4, k=0) p-values, where m2=m-p2 is the number of stochastic
trends in the asymptotic distribution
These results are reassuring, as they imply that the long-run parameter estimates in all
models are stable even though the sample period. It is noted that Hansen [1992] suggests
that the Lc test may also be viewed as tests for the null of cointegration against the
alternative of no cointegration. Thus, the test results also corroborate the previous
conclusion of cointegration among the variables under study.
5. Summary and Conclusions
This paper examines the impact of financial system with inflation, trade openness and
gross fixed capital formation on economic growth in the period of 2000:1 - 2011:4 for Iran.
The ARDL bounds testing approach to cointegration is used to investigate long-run
relationship among the variables. We used domestic credit provided by the banking sector
as a proxy for banking sector development and we used two proxies for stock market
development, namely stocks traded and market capitalisation.
Our findings confirmed long-run relationship between the variables. So, an attempt has
been made to estimate economic growth elasticity to financial system factors by applying
ARDL and DOLS procedures. The results further reveal that stock market and banking
sector development (commonly referred to as the financial system) have a statistically
significant and positive effect on economic growth for Iranian economy. The findings of
this paper reveal that stock market development is an important ingredient of growth in the
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Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
long-run, but with a relative lower magnitude as compared to the other determinants of
growth, particularly with banking development. These results support previous studies
such as Arestis, Demetriades and Luintel (2001) for five developed countries and
Seetanah et al. (2010) for developing countries.
According to the theories and previous empirical studies, other variables such as inflation,
trade openness and gross fixed capital formation affect the economic growth for Iranian
economy. The negative effect of inflation on economic growth in Iran support recent
studies such as Mahmoud Nia and Jafari (2013) for estimation threshold level of inflation
and its impact on economic growth in Iran.
Many policy implications could be drawn from this paper. First, financial system
developments in Iran can help to increase economic growth and able to exit country from
depression. Second, Iran has experienced huge inflation rates in recent years and this led
to decrease economic growth. Thus, Iranian policymakers must control and decrease
inflation rate. Third, we find that trade openness has a negative effect on economic growth
in Iran. This shows that high growth rate of import in Iran for recent years led to decrease
domestic productions and economic growth.
References
Adamopoulos, A., 2010. Stock market and economic growth: an empirical analysis for
Germany. Business and Economics Journal 2010 (BEJ-1).
Adjasi, C.K.D., Biekpe, N.B., 2006. Stock Market Development and Economic Growth:
The Case of Selected African Countries, African Development Review, Vol.18,
pp.144-161.
Allen, F., Gale, D., 2000. Comparing Financial Systems. MIT Press, Cambridge
Amable, B. and Chatelain, J.-B., 2001. Can financial infrastructures foster economic
development? Journal of Development Economics, Vol. 64, pp.481–498.
Arestis, P., Demetriades, P., Luintel, K.B., 2001. Financial development and economic
growth: the role of stock markets. Journal Money Credit Bank, Vol. 33, pp.16–41.
Atje, R., Jovanovic, B., 1993. Stock markets and development, European Economic
Review, Vol. 37, pp.632-40.
Bagehot, W., 1873, Lombard Street: A Description of the Money Market (Homewood, IL:
Richard D. Irwin).
Bekaert, G., Harvey, C. R., and Lundblad, C., 2001. Emerging equity markets and
economic development. Journal of Development Economics, Vol. 66, pp.465–504.
Beck, T., Levine, R., 2004. Stock markets, banks and growth: panel evidence. Journal
Banking and Finance, Vol. 28, pp.423–442.
Bencivenga, V.R., Smith, B.D., 1991. Financial Intermediation and Economic Growth.
Review of Economic Studies, Vol. 58, pp.195-209.
Ben Naceur , S. and Ghazouani ,S., 2007. Stock markets, Banks and Growth in some
Mena region countries. Research in International Business and Finance, Vol. 21,
pp.297–315.
Berthelemy, J., Varoudakis, A., 1996. Economic growth, convergence clubs, and the role
of financial development. Oxford Economic Papers, Vol. 48, pp.300– 328.
Bhide, A., 1993. The hidden costs of stock market liquidity. Journal Financial Economics,
Vol 34, pp. 31–51.
Boyd, J., Prescott, E., 1986. Financial intermediary-coalitions. Journal of Economic
Theory, Vol. 3, pp.211–232.
9
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
Capasso, S., 2008. Endogenous Information Frictions, Stock Market Development and
Economic Growth. The Manchester School, Vol. 76, pp. 204–222.
Cheung, Y., Lai, K.S., 1993. Finite-Sample Sizes of Johansen‘s Likelihood Ratio Tests for
Cointegration. Oxford Bulletin of Economics and Statistics, Vol. 55, pp. 315-328.
Christopoulos, D.K. and Tsionas E. G., 2004. Financial development and economic
growth: Evidence from panel unit root and cointegration tests. Journal of
Development Economics, Vol. 73, pp. 55‐74.
Cole, R. A., Moshirian, F., and Wu, Q., 2008. Bank stock returns and economic growth.
Journal of Banking and Finance, Vol.32, pp. 995–1007.
Demirg¨uc¸-Kunt, A., Levine, R., 1996. Stock market development and financial
intermediaries: stylized facts. World Bank Economic Review, Vol.19, pp.291–322.
Demirg¨u¸c-Kunt, A., Maksimovic, V., 1996. Stock market development and financing
choices of firms. World Bank Economic Review, Vol.10, pp.341–370
Filer, R.K., Hanousek, J., Campos, N.F., 1999. Do Stock Markets Promote Economic
Growth? Working Paper, No. 267.
Goldsmith, R.W., 1969. Financial Structure and Development. Yale University Press, New
Haven.
Hansen, B. E., 1992. Tests for parameter instability in regressions with I(1) processes,
Journal of Business and Economic Statistics, Vol.10, pp. 321–35.
Hicks, J., 1969, A Theory of Economic History (Oxford: Clarendon Press).
Hoshmand, M. Daneshnia, M. 2011. Impact of financial development on economic growth
in Iran. Scientific Journal of Agronomy and plant Breeding, Vol.18. pp.43-60
Jayaratne J. and Strahan P., 1996. The Finance‐Growth Nexus: Evidence from Bank
Branch Deregulation. Quarterly Journal of Economics, Vol. 111, pp. 639‐670.
King, R. G., Levine, R, 1993. Finance, entrepreneurship, and growth. Journal of Monetary
Economics, Vol. 32, 513–542.
Levine, R., 1991. Stock markets, growth and tax policy. Journal of Finance, Vol.46,
pp.1445–1465.
Levine, R., 2005. Finance and growth: theory and evidence. In: Aghion, P., Durlauf, S.
(Eds.), Handbook of Economic Growth. North-Holland, Amsterdam, pp. 865–934.
Levine, R., Zervos, S., 1996. Stock market development and long-run growth. World Bank
Economic Review, Vol.10, pp.323–339.
Levine, R., Loayza, N., Beck, T., 2000. Financial intermediation and growth: Causality and
causes. Journal of Monetary Economics, Vol. 46, pp. 31–77.
Levine, R., Zervos, S., 1998. Stock markets, banks and economic growth, American
Economic Review, Vol.88, pp.537-57.
Mahmoud Nia, D., Jafari, S., 2013. Threshold level of Inflation and Its Impact on Economic
Growth: A Case Study of Iran's Economy, Journal of Economic Development
Research, Vol. 2, pp. 133-156.
McKinnon, R., 1973, Money and Capital in Economic Development, (Washington: The
Brookings Institute).
Narayan, P.K. Narayan, S., 2013. The Short-run Relationship between the Financial
System and Economic Growth: New Evidence from Regional Panels. International
Review of Financial Analysis, Vol. 29, pp.70–78
Narayan, P. K. 2005. The Saving and Investment Nexus China: Evidence from
Cointegration Test. Applied Economics, Vol. 37, pp.1979-1990.
Pagano, M., 1993, Financial Markets and Growth: An Overview, European Economic
Review, Vol. 37, pp. 613 622.
Pardy, R., 1992. Institutional Reform in Emerging Securities Markets, World Bank Policy
Research Working Paper (Washington: World Bank).
10
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
Perman, R.J., 1991. Cointegration: An Introduction to the Literature. Journal of Economic
Studies, Vol.18, pp.3-30.
Pesaran, M.H., Shin, Y., 1995. An Autoregressive Distributed Lag Modeling Approach to
Cointegration Analysis. DAE Working Papers. No .9514, University of Cambridge.
Pesaran, M.H., Shin, Y., Smith, R.J., 2001. Bounds Testing Approaches to the Analysis of
Level Relationships. Journal of Applied Econometrics, Vol.16, pp.289-326.
Phillips, P.C.B., Perron, P., 1988. Testing for a Unit Root in Time Series Regression.
Biometrika 75, pp.335-346.
Reimers, H.E., 1992. Comparisons of Test for Multivariate Cointegration. Statistical
Papers, Vol.33, pp.335-359.
Reinsel, G.C., Ahn, S.K., 1992. Vector Autoregressive Models with Unit Roots and
Reduced Rank Structure: Estimation, Likelihood Ratio Test, and Forecasting. Journal
of Time Series Analysis, Vol.13, pp.353-375.
Rousseau, P.L. and S. Xiao, 2007. Banks, Stock Markets, and China‘s Great Leap
Forward, Emerging Markets Review, Vol. 8, pp. 206-217.
Rousseau, P.L., Wachtel, P., 2000. Equity markets and growth: cross-country evidence on
timing and outcomes, 1980–1995. Journal of Banking and Finance, Vol. 24, pp.
1933–1958.
Schumpeter, J., 1911, The Theory of Economic Development (Cambridge, MA: Harvard
University Press).
Seetanah, B., 2008. Stock Market Development and Economic Growth in Developing
Countries: Evidence from Panel VAR Framework [online]. CSAE Working Paper 041,
University of Oxford, UK. Availablefrom: http://www.csae.ox.ac.uk/conferences/
2010- EDiA/papers/041-Seetanah.pdf [Accessed 04 October 2010].
Stiglitz, J., 1985. Credit markets and the control of capital. Journal Money Credit Bank,
Vol.17, pp.133–152.
Shaw, E.S., 1973, Financial Deepening in Economic Development. Oxford University
Press, New York.
Shen,C.-H., Lee,C.-C. 2006, Same financial development yet different economic growth
— Why? Journal of Money, Credit, and Banking, Vol.38, pp.1907–1944.
Spears,A.,1991. Financial development and economic growth–causality tests. Atlantic
Economic Journal, Vol.19, pp.66–74.
Stock, J. H. and Watson, M. W. 1993. ‗Asimple estimator of cointegrating vectors in higher
order integrated systems‘, Econometrica , Vol. 61, pp. 783–820.
Wolde-Rufael, Y., 2009. Re-examining the Financial Development and Economic Growth
Nexus in Kenya. Economic Modeling, Vol. 26, pp.1140–1146.
Van Nieuwerburgh , S. , Buelens ,F., and Cuyvers, L . 2006. Stock market development
and economic growth in Belgium. Explorations in Economic History, Vol.43, pp.13–
38.
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