the causal relationship among trade openness, financial...

Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)

An Online International Research Journal (ISSN: 2306-367X) 2015 Vol: 4 Issue 1

1385

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The Causal Relationship among Trade Openness, Financial

Development and Economic Growth: Evidence from Kuwait

Afaf Abdull J. Saaed,

Associate Professor of applied Economics,

College of Business Administration,

American University in the Emirates,

United Arab Emirates.

[email protected] mail:-E

Majeed Ali Hussain, Associate Professor of Econometrics,

College of Business Administration

American University in the Emirates,

United Arab Emirates.

[email protected] mail:-E

_________________________________________________________________________

Abstract

The purpose of this paper was to examine empirically the causal relationship among financial

development (FD), trade openness (TO) and economic growth (GDP) by using vector

autoregressive (VAR) technique in Kuwait for the period 1977-2012. The econometric

methodology employed was the Cointegration and Granger Causality test. The stationarity

properties of the data and the order of integration of the data were tested using both the

Augmented Dickey-Fuller (ADF) test and the Phillip-Perron (PP) test. The variables tested

stationary at first differences. The Johansen multivariate approach to cointegration was

applied to test for the long-run relationship among the variables. Empirical results showed that

all variables are I (I) and are significant at 1percent. . Cointegration analysis suggests that

there is no cointegration vector among GDP, financial development and the degree of openness

of the economy. Granger causality tests based on VAR models show that there is a causal

relationship between economic growth and financial development and between the trade

openness of the economy and economic growth. Implying support for growth-led financial

development and support for trade of openness -led growth. Also, Money supply was the only

instrument of financial development that was seen to cause Trade openness.

__________________________________________________________________________

Keywords: Financial Development, Trade Openness, Economic Growth, Granger causality.

Kuwait.

JEL: O11, C22

mailto:[email protected]

mailto:[email protected]



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1. Introduction

Over the last decade much theoretical and empirical research has been directed to achieve a better

comprehension of the effects on welfare levels and rates of GDP growth of trade openness and of

financial development and integration. Several theoretical and empirical studies have devoted

considerable attention to the links between trade openness and economic performance, as well as to the

connections between financial market development and economic growth. Empirically a positive

relationship between openness and economic growth has been supported by Balassa (1985), the World

Bank (1987), Roubini and Sala Martin (1991), and Harrison (1995), Frankel and Romer (1996). As for

the relationship between financial development and growth compelling evidence of the existence of a

positive link has been obtained by: Atje Jovanovic (1993) and King and Levine (1992, 1993, 1994)

Jayaratne and Strahn (1996), Levine and Zervos (1996). Diaz-Alejandro (1985) (King and Levine, 1993;

Levine, 1997. However, literature has been relatively sparse in terms of the exact direct linkages among

trade openness, financial sector development, and financial openness.

The present study examines the causal relationship among financial development, trade openness

and economic growth in Kuwait using annual data from 1977 to 2012. Using Cointegration and Granger-

causality test in Vector Autoregressive Model (VAR) framework are employed to examine causal

relationship among trade openness, financial development and economic growth in Kuwait. Description

of data is presented first, and then procedure to examine stationarity of underlying time series is

described. Next, Johansen co-integration test is described followed by Granger-causality methodology

in VAR and followed by Wald test.

The main objectives of this study are examining empirically the causal relationship among financial

development, trade openness and economic growth in Kuwait during the period 1977-2012.

1.1 Hypothesis of the Study

In order to achieve our aim we can make the following hypotheses.

HN: Financial development does not causes economic growth

HA: Financial development increases and causes the economic growth.

HN: Trade openness has no positive relation with gross domestic product in long run

HA: Trade openness has positive relation with gross domestic product in long run.

HN: Financial development has no negative relation with trade openness

HA: Financial development has negative relation with trade openness

The remainder of this paper is organised as follows: Section 2 devoted for Literature Review. Section

3 describes the methodology, model specification and data. Section 4 reports the empirical results. A

detailed discussion and analysis of the results is also provided. Conclusion and policy implications are

given in section 6.




2. Literature Review

The relationship between financial development, trade openness and economic growth is an

important matter of discussion in economic literature. And, therefore, the relationship between trade

openness and financial development has been a subject matter for a substantial body of empirical work.

Their nexus is usually investigated in the empirical literature in two different lines: The first line of the

existing empirical research attempt to separately examine the importance of trade openness or financial

development on economic growth, the second line of the empirical works examines the relationship

between trade openness and the financial development collectively. With regard to methods haven used

to determine the importance of financial development and/or trade openness to economic growth, there

are two main methods. The first one employs simple or multiple regressions, while the second method

employs the causality technique. Recently, most of studies have attended to focus on VAR and VEC

models and cointegration approach. Our review of literature is limited to studies that focus on the joint

impact of both financial development and trade openness on economic growth.

Arouri et al (2013), attempted to explore the relationship between financial development, economic

growth and trade openness in case of Bangladesh over the period 1975Q1-2011Q4. The ARDL bounds

testing approach to cointegtaion and the innovative accounting approach for causality are used. Our

results show that financial development, trade openness and economic growth are linked over the long-

run. They find evidence in favor of the supply-side hypothesis while financial development and

economic growth cause exports.

Lacheheb, et al. (2013) examine the relationship between openness, financial development, and

economic growth in Algeria using the autoregressive distributed lag (ARDL) cointegration framework

for the period of 1980 to 2010. The results based on the bounds testing procedure confirm that a long-

run relationship between openness, financial development, and economic growth exist. Importantly, the

results of their study reveal that, openness has a significantly positive effect on economic growth. Broad

money which is a proxy for financial development is positive but insignificantly related to economic

growth. Also, both labour force and gross capital formation are insignificant. These findings suggest a

dire need for financial reforms in Algeria in order to improve efficiency in the financial sector so as to

stimulate saving/investment and thus, long-term economic growth.

Nazima, et'al (2013) conducted a study on analysing empirical relationship between trade openness,

industrial value added and economic growth in Pakistan They applied unit root test to determine the time

series properties while OLS technique of estimation and Granger causality tests were employed to find

out direction of causality. The results inferred from the econometric model articulated that imports and

exports (openness) affected positively economic growth. Attempts have also been made to investigate

the extent to which government activities affect economic growth.




Tash and Sheidaei (2012) analyze the joint impact of trade liberalization and financial development

on economic growth in Iran, using endogenous growth theory. The annual data employed covered the

period of 1966-2010. In this study, principal component analysis is applied to make better indexes for

trade liberalization, financial development and the joint effects of both. The empirical findings obtained

from Johansen co-integration procedure signify a positive relationship between trade liberalization,

financial development and the joint impact on economic growth in Iran.

Hanh (2010), investigate the linkages among financial development, financial openness and trade

openness in twenty-nine Asian developing countries over the period 1994-2008. Employing the Pedroni

co-integration technique, the research provides a number of major findings. The first one supports an

evidence of bidirectional causality between trade openness and financial development/openness. The

second one suggests that the between financial development and financial openness is heterogeneous,

as well as its variation across different measures.

Chimobi (2010) investigate financial development, trade openness and Economic Growth nexus in

Nigeria. The period covered was 1970-2005. The econometric methodology employed was the

cointegration and Granger causality to test for the long-run relationship among the variables but there

were no cointegrating relations between Growth, trade openness and the three measures of financial

development. The Granger-causality empirical findings suggest that trade openness and financial

development does have causal impact on economic growth. Conversely growth has causal impact on

trade and financial development, show support for growth-led trade but no support for trade-led- growth.

Banam (2010) analyzed the impact of financial liberalization on economic growth in Iran through

Johansen Co-integration test using time series data from 1965 to 2005 while also investigating the

determinants of economic growth. The financial liberalization index was represented by the financial

restraints index which includes interest rate controls, reserve requirements and directed credit multiplied

by -1. The results suggest that financial liberalization has positive and statistically significant impact on

economic growth measured by the gross domestic product in Iran. The findings provide support to

McKinnon (1973) and Shaw (1973), who argued that financial liberalization can promote economic

growth by increasing investment and productivity.

Drawing from Baltagi et al. (2009) and Hanh (2010) trade openness level is measured by the ratio

of the sum of imports and exports to GDP. This is the most widely used, simplest, and most intuitively

appealing measure of trade openness. Moreover, the measure is popular because data are readily

available for many countries and, as it is quite commonly used, it allows for comparability across studies.

Again, the trade openness data set is collected from the WDI.

The study analysing the causal linkages between trade openness and the Turkish economic growth

was put forward by Yucel (2009) who employed econometric methods (the Johansen and Juselius co

integration and Granger-Causality tests) during the period 1989- 2007. Results indicated that while trade




openness has a positive influence, financial development has a negative effect on growth. Moreover, the

Granger causality test results revealed the presence of bi-causal relationship between financial

development, trade openness and growth, thus favouring the view that economic policies directed at

financial development and trade openness have a statistically significant impact on economic growth.

Empirical studies have also begun to reveal the possible linkages among financial development,

financial openness and trade openness simultaneously Baltagi et al., (2009), using panel data of twenty

four countries during 1913-1999.

Although the theoretical literature assumes linkages both between trade liberalization and economic

growth and between finance and growth, the multi causal linkages between economic growth, financial

development, and international trade has recently attracted attentionRajan,and Zingales(2003),

emphasize the role of the supply-side factors and the resistance of incumbent industrialists and domestic

financial intermediaries who have a vested interest in a closed financial sector and therefore oppose the

developments in the financial market. It is argued that these incentives may be weakened with the

opening domestic financial sector to foreign competition and to international flows of capital. Chinn and

Ito (2006) investigate whether financial openness leads to financial development after controlling for

the level of legal development using a panel encompassing 108 countries over the period 1980 to 2000.

They argue that financial openness contributes to equity market development, but only when a threshold

level of general development of legal systems and institutions has been reached. Moreover, they find

that an increase in trade openness is a precondition for financial openness, and subsequently for financial

development. More recently, Baltagi et al. (2009) use modern panel data techniques to find out whether

indeed trade and financial openness can explain the recent growth in financial development, as well as

its variation across countries in recent year. They find that trade and financial openness are statistically

significant determinants of banking sector development.

Kar, et al., (2008) try to empirically estimate the joint impacts of trade liberalization and financial

development on economic growth for the period 1963-2005. Instead of using common proxies for the

issue, principal components analysis is employed to develop better measures (indexes) for trade

liberalization, financial development and the joint effects of both. The empirical results obtained from

the Johansen co-integration procedure show that trade liberalization, financial development and the joint

impacts of both positively contributed to economic growth.

Soukhakian (2007) also proposed a study that empirically investigated the causal relationship

between financial development, trade openness and economic growth in Japan covering the period 1960-

2003. Results showed that there was a long run equilibrium relationship between financial development,

trade and economic growth in Japan except between domestic credit (second measure of financial

development), trade and growth.




Abu-Bader and Abu-Qarn (2005) in an attempt to examine the relationship between financial

development and economic growth in Egypt, analysed time series annual data from 1960 to 2001 using

VAR methodology on four variables namely: Gross Domestic Product to measure economic growth and

ratio of money stock to nominal GDP, ratio of bank credit to the private sector to nominal GDP, ratio of

credit issued to non-financial private firms to total domestic credit, representing proxies for financial

development. Their findings show that the rise in private investment was facilitated by the financial

liberalization in 1990 which led to the rebound in economic performance of Egypt in the 1990s. Their

results infer that there is a direct linkage between financial development and financial liberalization

Wong Hock (2005,) investigated the impact of openness to international trade and financial

development on economic growth in Malaysia. An error correction model was estimated, which

indicated that openness to international trade has a significant impact on economic growth. Strong

evidences that openness to international trade Granger-causes economic growth and not vice versa was

also found.

Kingsley et al (2004) investigated the impact of openness on Nigeria’s long-run growth using the

cointegration approach. They tested for the number of cointegrating relationship between

LRGDP and LOPEN. They concluded that there is no significant relationship between openness and

economic growth, and that unbridled openness could have deleterious implications for growth of local

industries,

Sinha and Macri (2001) examine the relationship between financial development and economic

growth for eight Asian countries, which are divided in two categories. The first includes seven

developing countries while the second one includes only Japan. The aim of their study is to investigate

through a multivariate causality test if there are differences between financial development and

economic growth for both examined categories. The empirical results are mixed, namely there is a

bilateral causal relationship between the examined variables for India and Malaysia.

In summary, despite the truly enormous amount of research that has been undertaken on Financial

Development and economic growth there remain serious methodological issues. We could not find any

study that related to Kuwait, therefore, further studies are required in this field.

3. Methodology, Model Specification and Data

3.1 Methodology

In recent years there have been different empirical works which have shown that causation runs

from financial development to economic growth, that there is a bidirectional effect, or that economic

growth leads to financial development, some papers have even made a case for independent causation

between growth and finance. Several indicators of financial development have been proposed in the

literature. Different indicators will proxy different aspects of the relationship between the financial

system and economic performance. Verifying the relationship between financial development and




growth has at least two problems. First, it is necessary to assume a measure for financial development.

And, secondly, many econometrics articles about this lemma do not use a theoretical model. In relation

to the first problem, it will be used two variables as a proxy to financial development liquid liabilities

divided by GDP (usually M2 divided by GDP), liquid liabilities (M2). The proxy to growth indicator is

GDP

In the literature, the most commonly used measure of financial development is a ratio of some

monetary aggregates, usually M2, sometimes M2Y, M3 or M3-M1 to the GDP.

In the literature, the most commonly used measure of trade or economic openness is the ratio of sum

of imports and exports to GDP.

For trade openness we used the most common measure used in the literature, which is the ratio of

total trade (Exports + Imports) to GDP, King & Levine (1993), Murinde & Eng (1994), Siddiki (2002),

and Yucel (2009)). In the literature the most common measures for financial development are the M2 to

GDP ratio or the Domestic credit to private sector (% of GDP). In this study the first measure is used.

Following beyond literature, in this study, the proxy of trade openness calculated as the ratio of the

sum of the exports and imports to GDP and the proxy of financial development calculated as the ratio

of M2Y to GDP for Kuwait over the period 1977-2012.

For testing the causal relationships among trade openness, financial development and economic

growth in Kuwait, the Engle-Granger-causality test in Vector Auto regression (VAR) framework is

employed. Nevertheless, this approach has some prerequisites they must be satisfied (unit root test and

cointegration) in order to avoid invalid conclusions.

The plan of this study follows the following steps: In the first steps the nature of the data or order of

integration of the variables, is examined. This is because if the data is found to be non-stationary, as

most of the macroeconomic data happen to be, then application of OLS technique may give spurious

results. In order to avoid that, stationary test of the variables is required. For the purpose, Augmented

Dickey-Fuller test (ADF-test) and Philips-Perron test (PP test) have been applied. The ADF test is based

on the assumption that the error term is statistically independent and has a constant variance. Philips and

Perron (1988) developed a generalization of the ADF test procedure that allows for fairly mild

assumptions concerning the distribution of errors. While the ADF test corrects for higher order serial

correlation by adding the lagged difference term on the right hand side, the PP test makes a correction

to the t-statistics of the coefficient from the AR(1) regression to account for the serial correlation in

residual term. So, the PP statistics are just modification of the ADF t-statistics that takes into account

less restrictive nature of the error process. For the reason, the present study has also conducted PP test

to examine the stationary nature of the variables under consideration. Once the order of integration is

known and it is found that all the variables are integrated of order one I(1), the presence of long run

relationship is examined with the help of the maximum-likelihood test procedure established by




Johansen and Juselius (1990) and Johansen (1991).multivariate cointegration test. So long as the

dependent variable is integrated of order one and explanatory variables are integrated of order one, there

can be a long run relationship between these variables provided that they are cointegrated.In order to

investigate the presence of long run equilibrium relationship (cointegration) among these variables

through Johansen and Juselius test approach, following Vector Auto Regressive Model (VAR).

3.2 Model Specification

The empirical model used to test whether FD cause GDP, Whether GDP Cause TO or whether a

one-way or a two way causal relationship exist between GDP and FD and GDP and TO. In this study

we apply Granger causality within VAR to identify the relationship between GDP, FD and TO.

The Functional form is:

GDP = f (FD, TO) (1 )

The function can also be represented in a log-linear econometric format thus:

L )2(10 tttt TOLLFDGDP

Where LGDP= Log (Gross Domestic Product),LFD= Log (Financial Development)

LTO= Log trade of openness

termconstanttheis0 , t is the time trend, is a random error term.

In the literature, there are perhaps two broad types of econometric techniques regarding the trade

openness, financial development and economic growth nexus: first one is the traditional OLS regression,

and the other is the cointegration and Granger no-causality approach based on VAR or VECM models.

To estimate the econometric model, the theory of co-integration has been used for this purpose. It seems

efficient to test the relationships between study variables and empirically validate the results obtained

after carrying out the statistical tests applied to the model. Engle and Granger (1987) presented the theory

of co-integration in which a stationary linear combination can be interpreted as a relationship of long-

term equilibrium between the variables studied. This research utilizes the second technique. Checking

for cointegration properties of the series of interest prior to testing for causality is therefore an important

first step. Then if the variables are cointegrated, an Error Correction model should be used. VAR model

is used when there is no cointegration among the variables and it is estimated using time series that have

been transformed to their stationary values. The formulation of VAR model with three variables to be

tested would be:

3.3 VAR and Granger-Causality

Granger causality tests are conducted to determine whether the current and lagged values of one

variable affect another. One implication of Granger representation theorem is that if two variables, say

Xt and Yt are co integrated and each is individually 1(1), then either Xt must Granger-cause Yt or Yt

must Granger-cause Xt. This causality of co-integrated variables is captured in Vector Error Correction

Model (VECM). In a VECM long and short-run parameters are separated. In the present study linear




combinations of non-stationary variables are not found stationary, that is, the variables are not

cointegrated. In absence of co-integration the unrestricted VAR in first difference is estimated, which

take the following form:

5

4

)3(

3

1

4

1

1

1

1

2

1

3

1

1

1

1

1

1

2

1

1

1

1

tit

k

i

it

k

i

it

k

i

t

tit

k

i

it

k

i

it

k

i

t

tit

k

i

it

k

i

it

k

i

t

TOFDJGDPGTO

TOFDNGDPMFD

TOFDGDPGDP

Where GDPt , FDt and TOt are represents economic growth and financial deepening and trade openness

respectively. ∆ is the difference operator; and, and t3 the serially uncorrelated error terms in

equation (3), (4) and (5): k, is the numbers of optimum lag length, which is determined empirically by

Schwarz criterion (SC), 1 1, M1 and N1, G1 and J1 are all short run coefficients to be estimated. For

each equation in the above VAR, Wald χ2 statistics is used to test the joint significance of each of the

other lagged endogenous variables in that equation. For tGDP to be unaffected by FD t and ∆TOt,

Σα1 and Σα2, respectively must not be significantly different from zero. Similar logic applies to FDt and

TOt. and, and t3 the serially uncorrelated error terms.

3.4 Data Analysis

The objective of this paper is to investigate the relationship between economic growth, financial

development and trade openness in Kuwait using the annual data for the period from 1977 to 2012,

which includes the 35 annual observations. Data for our variables obtained from the country tables

published by the International Financial Statistics (IFS) and the International Monetary Fund, World

Development Indicator (WDI).,On-line, 2014 (www.worldbank.org ). All the variables are expressed in

natural logarithm for the usual statistical reasons.

4. Stationarity Test

4.1 Unit root tests

The first step involves testing the order of integration of the individual series under consideration.

Researchers have developed several procedures for the test of order of integration. The most popular

ones are Augmented Dickey-Fuller (ADF) test due to Dickey and Fuller (1979, 1981), and the Phillip-

Perron (PP) due to Phillips (1987) and Phillips and Perron (1988). Augmented Dickey-Fuller test relies

on rejecting a null hypothesis of unit root (the series are non-stationary) in favor of the alternative

hypotheses of stationarity. The tests are conducted with and without a deterministic trend (t) for each of

the series. The general form of ADF test is estimated by the following regression

)6(1

11

ti

n

i

tt

t1 t2

t1 t2




Where: Y is a time series is a linear time trend, is the first difference operator, is a constant , n is

the optimum number of lags in the dependent variable and is the random error term; The coefficients

α, β and β1, are being estimated. And the Phillip-Perron is equation is thus

+ 1t t (7)

Many macroeconomic time series contain unit roots dominated by stochastic trends as developed by

Nelson and Plosser (1982). Unit roots are important in examining the stationarity of a time series because

a non-stationary repressor invalidates many standard empirical results. The presence of a stochastic trend

is determined by testing the presence of unit roots in time series data. A unit root test is performed using

Augmented Dickey-Fuller (ADF) (1979), and Kwiatkowski et al. (1992).

4.2 Johansen Cointegration Test

Having confirmed the stationarity of the variables at I (1), we proceed to examine the presence or

no presence of cointegration among the variables. When a cointegration relationship is present, it means

that GDP, FD and TO share a common trend and long-run equilibrium as suggested theoretically. We

employ the maximum-likelihood test procedure established by Johansen and Juselius (1990) and

Johansen (1991) multivariate cointegration test. The procedure involves the identification of rank of the

m-by-m matrix in the specification given by:

(8)

Where a column vector of the m variables is, represents coefficient matrices,

is a difference operator, k denotes the lag length, and is a constant. If has zero rank, no

stationary linear combination can be identified. In other words, the variable are non-coin grated. If

the rank r and is greater than zero, however, there will exist r possible stationary linear combinations

and may be decomposed into two matrices and , (each m x r) such that = In this

representation b contains the coefficient of the r distinct co integrating vectors that render

stationary, even though is itself non-stationary contains the speed of the adjustment coefficients

for the equation. Johansen and Juselius (1990), using maximum likelihood, have developed two

Statistics to test the null of no cointegration. These statistics are the Trace statistic and the maximal

eigenvalue statistic (Max-L), and computed as follow where r is the number of cointegration vectors

and are the N square canonical correlations between Xt-p and Xt, the series being ranged in

a decreasing order so that for i > j Critical values are in Osterwald-Lenum (1992). If the

computed statistics is lower than the critical value, one can reject the null hypothesis of no cointegration.

t

1

1

k

i

ikiitit u

t

t

t

t

N11

ji

and




5. Empirical Analysis

Table 1 reports the descriptive statistics for the sample of the three variables under investigation.

Overall, calculations indicate that GDP and TO are not normally distributed except for FD and are

characterized as positively skewed.

Table 1: Descriptive Statistics

TO GDP FD

Mean 0.228778 440936.8 290414.7

Median 0.196298 255712.4 278872.5

Maximum 0.384005 1477594. 541655.4

Minimum 0.137663 92040.80 90853.47

Std. Dev. 0.080288 406021.8 117998.4

Skewness 0.807844 1.281520 0.569791

Kurtosis 2.203962 3.303303 3.104047

Jarque-Bera 4.866188 9.991749 1.964209

Probability 0.087765 0.006766 0.374522

Sum 8.236017 15873724 10454930

Sum Sq. Dev. 0.225615 5.77E+12 4.87E+11

Observations 36 36 36

Source: Eviews version 8

5.1 Test of Unit root (ADF)

Our aim in this study is to establish whether there is causality relationship between trade openness,

financial development and growth of Gross domestic product (GDP) Kuwait economy. The data for the

analysis consist of annual observations for the period 1977-2012. Testing for Stationary: The unit root

tests are important in identifying the stationary trend of a time series data. It is vital to apply unit root

test in order to avoid specious results as non-stationary data invalidate the normal statistical tests. This

research applied two tests of unit root data which is the Augmented Dickey-Fuller test (ADF) and the

Phillips- Perron (PP) test statistics to observe the integrated order and stationary behaviour of data. To

investigate stationarity properties of the variables under consideration (GDP, financial development and

trade openness) we carry out a univariate analysis for testing the presence of a unit root.

Table 2

Variables ADF PP Order of Integration

Level 1st Diff

Level 1st Diff

LGDP 0.5715 (0.0000)***

0.5715 0.0000*** I(1)

LFD

(0.6751) (0.0002)*** 0.5715 (0.0000 )***

I(1)

LTO

(0.0466)** ( 0.0000)*** 0.0466 ** 0.0000*** I(1)

Note: (1) ***, ** and *denotes significant at 1%, 5% and 10% level respectively. and in PP test it

is based on Newey-West using Bartlett kernel. Source: Eviews version 8

Table 2 reports the results of Augmented Dickey-Fuller (ADF) and Philips Perron test statistic of

Unit Root it is essential to check for the stationarity of the data series used. This is important to obtaining

an unbiased estimation from the Granger causality tests, and also because the test is used only when




variables are 1(0) or 1(1). The Augmented Dickey-Fuller (ADF) test was applied to test for the existence

of unit root tests. Therefore, Table 2 presents the results of Augmented Dickey- Fuller unit root test and

the Phillips- Perron (PP) on the variables at their level and difference values. The summary of the result

reveals that all the variables are non-stationarity in the level data except for TO. However, the

stationarity property is found after taking the first difference of the variables in 1% critical level.

5.1.1 Selection of lag length

Vector auto regression (VAR) is an econometric model that is utilized for the understanding of the

linear relationships among variables with multiple time series. Models included in VAR simplify the

auto regression models by allowing the impact for more than one changing variable on relevant time

series data. The preliminary task in estimating the VAR model is to determine the optimum order of lag

length. This is important since under parameterization may lead to estimation bias and over

parameterization results in the loss of degrees of freedom and thus the power of the test. In order to

select the lag length of the VAR model the selection criteria is used, Sequential Modified Likelihood

Ratio (LR), Final Prediction Error (FPE), Akaike Information Criterion (AIC), Schwarz Information

Criterion (SIC) and Hannan-Quinn Information Criterion (HQ) are employed. It is clear from Table 3

that LR, FPE, AIC, SC, HQ and HQ statistics are chosen lag 1for each endogenous variable in their

autoregressive and distributed lag structures in the estimable VAR model. Therefore, lag of 1 is used for

estimation purpose.

Table 3:VAR Lag Order Selection Criteria

Lag LogL LR FPE AIC SC HQ

0 -32.47222 NA 0.001723 2.149832 2.285878 2.195607

1 83.67096 204.1304* 2.62e-06 -4.343695 -3.799510* -4.160593*

2 94.40116 16.90818 2.39e-06*

-

4.448555* -3.496232 -4.128127

3 101.2950 9.609644 2.83e-06 -4.320911 -2.960450 -3.863157

* indicates lag order selected by the criterion

LR: sequential modified LR test statistic (each test at 5% level)

FPE: Final prediction error

AIC: Akaike information criterion

SC: Schwarz information criterion

HQ: Hannan-Quinn information criterion


5.2 Cointegration Test

As the econometric analysis suggests, when the concern of unit root has been addressed, the co-

integration test can be applied to verify the existence of long run relationship. The theory of co-

integration defines that even though the variables under consideration are non-stationary at individual

level but the linear relationship among them may still be stationary. After confirming the stationarity of

the variables at 1(1). We started the cointegration analysis by employing the Johansen and Juselius

(1990) multivariate cointegration test. This technique observes the long run relationship among the non-




stationary variables while showing number of co-integrating equations. The test is based on the

comparison of H0 (r=0) against the alternative H1 (r^0) where"r"represents the number of co integrating

vectors.

Table 4: Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None 0.360461 20.15251 29.79707 0.4126

At most 1 0.135133 4.954268 15.49471 0.8137

At most 2 0.000534 0.018150 3.841466 0.8927

Trace test indicates no cointegration at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

Table 5:Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None 0.360461 15.19824 21.13162 0.2752

At most 1 0.135133 4.936119 14.26460 0.7499

At most 2 0.000534 0.018150 3.841466 0.8927

Max-eigenvalue test indicates no cointegration at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values


Table 4 and 5 reported the result from the cointegration tests. Evidence from the result suggests that

the null hypothesis of no co-integration (r =0) cannot be rejected. Based on these results, we cannot find

a cointegrating. This in effect suggests that the existence of long-run relationship between the variables

employed in our study is not confirmed.

This is quite interesting because economic variables that are non-stationary tend to move together

in the long run—a desirable property of cointegrated variables. Besides, the fact that most studies found

cointegration relationships between these variables makes this finding more stunning. The results

confirmed that there is no long run cointegrating relationships between the three variables. In other

words, financial development, Openness and Growth do not share a common trend and long-run

equilibrium for the Kuwait.

The desire to pursue the dynamic long run relationships between the three variables has been

dampened because no cointegrating relationships were established. The study failed to establish any

viable indication of the existence of causal linkage between these variables. To be able to proceed to the

next step, the study discards the error correcting term in the proposed trivariate granger causality

framework.




5.3 VAR and Granger-Causality Test

It was established in Section 1.5.2 that Growth, Openness and financial development are not

cointegrated in UAE. The paper, thus, has no justification for testing Granger causality in a trivariate

vector error correction model (VECM). The variables are, by theory, differenced to ensure that they are

stationary. The results of unit root tests indicated that the three variables are stationary in their first

differences. The paper then conducted Granger causality tests in a trivariate VAR framework and the

results are presented in Table 6.

Granger Causality test is widely used by researchers to determine the causal relationship among the

variables. This test has other advantages that it also specifies the direction of the causality. Having found

no cointegration among the variables (GDP,FD and TO) we carried out the Granger-causality by the

mean of VAR. The results are reported in Table no. 6.

Table 6: Pairwise Granger Causality Tests

Sample: 1977 2012, Lags: 2

Null Hypothesis: Obs F-Statistic Prob.

LFD does not Granger Cause LGDP 34 1.15504 0.3291

LGDP does not Granger Cause LFD 7.00448 0.0033

LTO does not Granger Cause LGDP 34 5.30895 0.0108

LGDP does not Granger Cause LTO 0.36461 0.6976

LTO does not Granger Cause LFD 34 1.39880 0.2631

LFD does not Granger Cause LTO 1.33206 0.2796


The next step is the Granger test to determine the Pair-wise causal relationship between the variables.

Our results suggest that the null hypothesis that GDP does not Granger cause FD is rejected which

indicates causality running from economic growth to financial development. In addition, there is a

relationship between GDP and TO and there aren’t any relationship between TO and FD.

5.3.1 Variance Decomposition Analysis

The results of variance decomposition analysis, which measures the percentage of a variable's

forecast error variance that occurs as the result of a shock from a variable in the system, are presented

in Table 7. The test yield different results based on the ordering. In this study, however, the variables

were arranged in the following order LGDP LTO LFD. In overall, the forecast horizon is for 10 years

and the contribution of each variable own shock and to the shocks of other variables in the system are

reported. The variance decomposition analysis indicates that trade openness and financial development

are the most exogenous variables. A high proportion of their shocks are explained by their own

innovations compared to the contributions of own shocks to innovations for economic growth. The

proportion of variance of economic growth explained by its own decreases over time and reaches




62.37621 per cent at the end of the period. Trade openness shock accounts for 0 percent in the first year.

Its proportion increases over time and reaches 33.84 per cent in horizon number 10.The role played by

financial development shock proportion increases and accounts for 3.77 per cent in the last period.

Financial development shock, which is assumed to account for the whole variance of financial

development the first year, continuously dominates in the following years. Its proportion sharply

increases over time, but still accounts for 13.68 per cent in the 10th year. In the long run, trade openness

shock is an important source of financial development variability. The role played by trade openness

shock decreases over time and accounts for 68.79643 per cent in the last year. Its proportion increases

over time and reaches 25.96 per cent in the last year. The estimated results from variance decomposition

demonstrate the trade openness -led growth hypothesis and financial development led growth.

There is causality of financial development on GDP growth, but this impact is weak. In summary,

trade openness shock is the most important source of shock to GDP and financial development. Shock

to trade openness is important sources of variability for its own at first, but this self-effect diminishes in

a very small portion.

Table 7: Variance Decompositions

Variance Decomposition of LGDP:

Period LGDP LTO LFD

1 100.0000 0.000000 0.000000

2 92.72948 5.215904 2.054620

3 81.06054 15.29669 3.642765

4 72.96384 22.84194 4.194218

5 68.43383 27.32493 4.241238

6 65.88731 30.03065 4.082037

7 64.38052 31.75209 3.867383

8 63.44762 32.85266 3.699716

9 62.83286 33.51419 3.652945

10 62.37621 33.84860 3.775181




5.3.2 VAR Stability Test

CUSUM and CUSUM SQ tests are applied to examine the stability of the long-run coefficient

together with short run dynamic (Pearson and Pearson, 1997). The test is proposed by Brown, Durbin

and Evans (1975) to assess the parameters constancy. The test is applied to the residuals of all variables

in the VAR model. If the plot of the CUSUM statistics lies within the critical bound of 95% level of

significance represented by a pair of straight lines drawn at 95% level of significance the null hypothesis

relating to all coefficients in the VAR model cannot be rejected. If any of the lines is crossed. The null

hypothesis of coefficient constancy at 95% level of significance will be rejected, implying that the

recursive residuals have zero expected value. A CUSUM-SQ test is based on the square recursive

residuals; a similar procedure is followed to perform the test. Other testes would be performed such as:

VAR residual serial correlation LM test; VAR residual normality; and Stability or stationary of estimate.

The Figure 1 specifies that plots for CUSUM and CUSUMSQ are between critical boundaries at 5%

level of significance. This confirms the accuracy of long run and short run parameters which have impact

on economic growth in Kuwait. This indicates that model seems to be steady and specified appropriately.

Accordingly we can conclude that for our model the estimated VAR is stable or stationary.

Variance Decomposition of LTO

Period LGDP LTO LFD

1 15.48432 84.51568 0.000000

2 13.77690 86.22137 0.001734

3 17.28485 81.94338 0.771774

4 18.87025 78.16581 2.963937

5 19.01550 75.40264 5.581860

6 18.72120 73.35515 7.923654

7 18.36845 71.78053 9.851018

8 18.04490 70.54533 11.40978

9 17.76285 69.57047 12.66668

10 17.52190 68.79643 13.68168

Variance Decomposition of LFD

Period LGDP LTO LFD

1 92.94094 0.018618 7.040447

2 73.48562 4.103569 22.41081

3 55.28968 12.25358 32.45675

4 44.08144 18.18854 37.73002

5 37.49112 21.55999 40.94889

6 33.32621 23.46371 43.21008

7 30.49535 24.59601 44.90864

8 28.47220 25.29156 46.23624

9 26.97399 25.71685 47.30916

10 25.83223 25.96630 48.20147

Cholesky Ordering: LGDP LTO LFD




Figure 1: Plot of CUSUM and Cumulative Sum of Squares of Recursive Residuals

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

86 88 90 92 94 96 98 00 02 04 06 08 10 12

CUSUM of Squares 5% Significance

CUSUMSQ FD EQUATION

-16

-12

-8

-4

0

4

8

12

16

86 88 90 92 94 96 98 00 02 04 06 08 10 12

CUSUM 5% Significance

CUSUM statistics for TO equation

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

86 88 90 92 94 96 98 00 02 04 06 08 10 12


CUSUM of SQ GDP equation

Note: The straight lines represents critical bounds at 5% significance level.


1 .6

1.

6

1

.6

-16

-12

-8

-4

0

4

8

12

16

86 88 90 92 94 96 98 00 02 04 06 08 10 12

CUSUM 5% Significance

CUSUM statistics for GDP equation

CUSUM-SQ statistics for FD equation

CUSUM of Squares 5 % Sign if can ce

1.2

0.4

0.

0

- 0.4

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

86 88 90 92 94 96 98 00 02 04 06 08 10 12


CUSUM-SQ statistics for TO equation




In addition to the above tests we perform the Jarque-Bera test. According to the result we can

reject the hypothesis of normality properties; since P-value is 0.041 this provides some support for

the hypothesis those residuals from our VAR model are not normally distributed. The Lag order

selection shows that lag intervals of (1, 2) is proper selection. However, the model are not serially

correlated also the model free from heteroscedasticity and ARCH affect (see table 4A, 5A and 6A in

appendix A).

6. Conclusion and Policy Implications

The study began by empirically examining the causal relationship between financial development,

trade openness and economic growth in Kuwait during the period 1977-2012. The study uses

multivariate VAR framework.

The analysis started with stationary property examination of the underlying time series data. The

estimated results confirmed that GDP, TO and FD are non-stationary at the level data but stationary at

the first differences. Hence, they are integrated of order one I (1).The Johansen's multivariate

cointegration test evidence from the result suggests that the null hypothesis of no co-integration (r =0)

cannot be rejected. Based on these results, we cannot find a cointegrating. This in effect suggests that

the existence of long-run relationship between the variables employed in our study is not confirmed.

Having found no cointegration among the variables (GDP, FD and TO) we carried out the Granger-

causality by the mean of VAR.The result shows the existence of unidirectional causality between

economic growth and financial development., and shows the existence of unidirectional causality

between the degree of trade openness and economic growth .Meaning that the economic growth Granger

cause financial development and the degree of trade openness Granger cause economic growth. But the

degree of trade openness does not Granger cause financial development and financial development does

not Granger cause the degree of trade openness.

The above findings clearly indicate that financial deepening plays a role in contributing financial

development, globalization and economic growth, both directly and indirectly. This suggest that there

is need of reforming Kuwait financial system. Thus, Kuwait should promote its trade linearization

policy, in order to enhance both growth of GDP and financial development.

6.1 Implications

The findings of the study show that economic growth has impact on the financial development. Also,

trade of openness revealed a direct impact on economic growth, enhancing the performance of the

financial sector performance as well as increasing the strength of Kuwait participating in international

trade (openness). If this is seriously considered by policy makers, its pertinent that policies enhance

growth-led financial deepening is seriously pursued for Kuwait to participate and benefit effectively

from international trade. Furthermore, the effort should be made to take on measures that lead to GDP

growth through other measures including trade openness. The above findings clearly indicate that




financial deepening plays a role in contributing financial development, globalization and economic

growth, both directly and indirectly. This suggests that there is need of reforming Kuwait financial

system. Thus, Kuwait should promote its trade linearization policy, in order to enhance both growth of

GDP and financial development.

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Appendix A Table 1A:

Vector Error Correction Estimates

Sample (adjusted): 1980 2012

Included observations: 33 after adjustments

Standard errors in ( ) & t-statistics in [ ] Cointegrating Eq: CointEq1 LGDP(-1) 1.000000

LFD(-1) -5.026859

(1.72829)

[-2.90857]

LTO(-1) -42.25577

(10.0417)

[-4.20804]

C -25.53731 Error Correction: D(LGDP) D(LFD) D(LTO) CointEq1 -0.033558 0.041996 0.016897

(0.01513) (0.01534) (0.00531)

[-2.21831] [ 2.73778] [ 3.18497]

D(LGDP(-1)) -0.429035 0.901124 0.277803

(0.77898) (0.78987) (0.27319)

[-0.55077] [ 1.14085] [ 1.01689]

D(LGDP(-2)) -0.158107 0.253227 0.052448

(0.74947) (0.75994) (0.26284)

[-0.21096] [ 0.33322] [ 0.19954]

D(LFD(-1)) -0.580174 1.096875 0.261725

(0.75225) (0.76277) (0.26382)

[-0.77125] [ 1.43802] [ 0.99208]

D(LFD(-2)) -0.113506 0.291346 -0.066483

(0.73806) (0.74838) (0.25884)

[-0.15379] [ 0.38930] [-0.25685]

D(LTO(-1)) -0.350843 0.662395 0.147264

(0.64983) (0.65892) (0.22790)

[-0.53990] [ 1.00527] [ 0.64618]

D(LTO(-2)) -0.185356 0.349507 0.246115

(0.53982) (0.54737) (0.18932)

[-0.34337] [ 0.63852] [ 1.30003]

C 0.131132 -0.099111 -0.025124

(0.07407) (0.07511) (0.02598)

[ 1.77031] [-1.31957] [-0.96715] R-squared 0.228199 0.313785 0.467012

Adj. R-squared 0.012095 0.121645 0.317776




Sum sq. resids 1.473745 1.515246 0.181258

S.E. equation 0.242796 0.246191 0.085149

F-statistic 1.055966 1.633107 3.129344

Log likelihood 4.468593 4.010368 39.04664

Akaike AIC 0.214025 0.241796 -1.881615

Schwarz SC 0.576814 0.604586 -1.518825

Mean dependent 0.081346 -0.000669 0.002932

S.D. dependent 0.244277 0.262686 0.103090 Determinant resid covariance (dof adj.) 1.22E-06

Determinant resid covariance 5.29E-07

Log likelihood 97.98578

Akaike information criterion -4.302168

Schwarz criterion -3.077753




Table 2A and 3A:

Pairwise Granger Causality Tests

Sample: 1977 2012

Lags: 3

Null Hypothesis: Obs F-Statistic Prob. LFD does not Granger Cause LGDP 33 0.65489 0.5871

LGDP does not Granger Cause LFD 1.26715 0.3062 LTO does not Granger Cause LGDP 33 3.10636 0.0438

LGDP does not Granger Cause LTO 1.75248 0.1810 LTO does not Granger Cause LFD 33 0.52816 0.6669


Pairwise Granger Causality Tests

Sample: 1977 2012

Lags: 4

Null Hypothesis: Obs F-Statistic Prob. LFD does not Granger Cause LGDP 32 1.09407 0.3829

LGDP does not Granger Cause LFD 1.44891 0.2501 LTO does not Granger Cause LGDP 32 2.73897 0.0534

LGDP does not Granger Cause LTO 3.39687 0.0253 LTO does not Granger Cause LFD 32 0.93698 0.4602



Figure 2:

0

2

4

6

8

10

12

14

-0.4 -0.2 0.0 0.2 0.4 0.6 0.8

Series: ResidualsSample 1979 2012Observations 34

Mean -9.39e-16Median -0.019798Maximum 0.711088Minimum -0.486763Std. Dev. 0.203760Skewness 1.055275Kurtosis 6.339822

Jarque-Bera 22.11252Probability 0.000016





Table 4A:

Breusch-Godfrey Serial Correlation LM Test: F-statistic 0.794963 Prob. F(2,25) 0.4627

Obs*R-squared 2.033007 Prob. Chi-Square(2) 0.3619

Test Equation:

Dependent Variable: RESID

Method: Least Squares

Date: 02/03/15 Time: 01:30

Sample: 1979 2012

Included observations: 34

Presample missing value lagged residuals set to zero. Variable Coefficient Std. Error t-Statistic Prob. C(1) 0.958571 1.256526 0.762874 0.4527

C(2) -0.913097 1.211973 -0.753397 0.4582

C(3) 0.399199 0.683787 0.583806 0.5646

C(4) -0.515867 0.763335 -0.675807 0.5054

C(5) 0.019767 0.532203 0.037142 0.9707

C(6) -0.717536 0.803777 -0.892705 0.3805

C(7) -1.180133 1.865889 -0.632478 0.5328

RESID(-1) -0.600014 0.738141 -0.812872 0.4240

RESID(-2) -0.271106 0.248413 -1.091352 0.2855 R-squared 0.059794 Mean dependent var -9.39E-16

Adjusted R-squared -0.241071 S.D. dependent var 0.203760

S.E. of regression 0.226995 Akaike info criterion 0.094153

Sum squared resid 1.288172 Schwarz criterion 0.498189

Log likelihood 7.399404 Hannan-Quinn criter. 0.231941

F-statistic 0.198741 Durbin-Watson stat 2.129683

Prob(F-statistic) 0.988428





Table 5A:

Heteroskedasticity Test: Breusch-Pagan-Godfrey F-statistic 0.156099 Prob. F(6,27) 0.9861


Scaled explained SS 1.919209 Prob. Chi-Square(6) 0.9270

Test Equation:

Dependent Variable: RESID^2


Date: 02/03/15 Time: 01:31

Sample: 1979 2012

Included observations: 34 Variable Coefficient Std. Error t-Statistic Prob. C -0.068321 0.653846 -0.104491 0.9176

LGDP(-1) 0.022697 0.275146 0.082489 0.9349

LGDP(-2) -0.018036 0.270023 -0.066794 0.9472

LFD(-1) 0.017979 0.243058 0.073971 0.9416

LFD(-2) -0.010424 0.249861 -0.041717 0.9670

LTO(-1) 0.145243 0.239655 0.606050 0.5495

LTO(-2) -0.190234 0.230641 -0.824805 0.4167 R-squared 0.033526 Mean dependent var 0.040297


S.E. of regression 0.102728 Akaike info criterion -1.532225

Sum squared resid 0.284931 Schwarz criterion -1.217975

Log likelihood 33.04783 Hannan-Quinn criter. -1.425057







Table 6A:

Heteroskedasticity Test: ARCH F-statistic 0.090498 Prob. F(1,31) 0.7656


Test Equation:

Dependent Variable: RESID^2


Date: 02/03/15 Time: 01:32

Sample (adjusted): 1980 2012

Included observations: 33 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C 0.043530 0.018482 2.355296 0.0250

RESID^2(-1) -0.053985 0.179456 -0.300828 0.7656 R-squared 0.002911 Mean dependent var 0.041293


S.E. of regression 0.097194 Akaike info criterion -1.765522

Sum squared resid 0.292847 Schwarz criterion -1.674825

Log likelihood 31.13112 Hannan-Quinn criter. -1.735005




the causal relationship among trade openness, financial...

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