The Causal Relationship among Trade Openness, Financial Development and Economic Growth: Evidence from Kuwait

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. E-mail: afaf1955@yahoo.com Majeed Ali Hussain, Associate Professor of Econometrics, College of Business Administration American University in the Emirates, United Arab Emirates. E-mail: mhussain1950@gmail.com 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 1385

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 Grangercausality 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. H N: Financial development does not causes economic growth H A: Financial development increases and causes the economic growth. H N: Trade openness has no positive relation with gross domestic product in long run H A: Trade openness has positive relation with gross domestic product in long run. H N: Financial development has no negative relation with trade openness H A: 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. 1386

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 longrun. 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 longrun 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. 1387

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 1388

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. 1389

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 1390

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 1391

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 GDP LFD LTO (2) t 0 t 1 t t Where LGDP= Log (Gross Domestic Product),LFD= Log (Financial Development) LTO= Log trade of openness 0 is the constant term, 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 longterm 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 1392

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: GDP FD TO t t t k i1 k i1 k i1 GDP 1 1 1 M GDP G GDP ti ti ti i1 k i1 i1 k k FD 1 1 N FD 1 J FD ti ti ti i1 k k k i1 i1 TO 4 2 TO 3 TO Where GDP t, FD t and TO t are represents economic growth and financial deepening and trade openness respectively. is the difference operator; and1t, ti ti ti 3t 1t 2t (3) 4 and 3t 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, M 1 and N 1, G 1 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 GDPt to be unaffected by FD t and TO t, Σα 1 and Σα 2, respectively must not be significantly different from zero. Similar logic applies to FD t and TO t. and1t, 2 t 3.4 Data Analysis and 3t the serially uncorrelated error terms. 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 2 t 5 t n t1 1i t i1 (6) 1393

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 β 1, are being estimated. And the Phillip-Perron is equation is thus t + t1 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: t 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 t k 1 i1 i ti ik u 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 i and t (8) representation b contains the coefficient of the r distinct co integrating vectors that render t 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 1 1 N t are the N square canonical correlations between X t-p and X t, the series being ranged in a decreasing order so that i j 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. 1394

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 1 st Diff Level 1 st 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 1395

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 Cr 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* - 4.448555* -3.496232-4.128127 2 94.40116 16.90818 2.39e-06* 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 Source: Eviews version 8 5.2 Cointegration Test As the econometric analysis suggests, when the concern of unit root has been addressed, the cointegration test can be applied to verify the existence of long run relationship. The theory of cointegration 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- 1396

stationary variables while showing number of co-integrating equations. The test is based on the comparison of H 0 (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 Source: Eviews version 8 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. 1397

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 Source: Eviews version 8 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 1398

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 1399

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 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. 1400

Figure 1: Plot of CUSUM and Cumulative Sum of Squares of Recursive Residuals CUSUM-SQ statistics for FD equation 1. 2 0. 4 0. 0-0.4 of Squares 5 % Sign if can ce CUSUMSQ FD EQUATION 1.4 CUSUM statistics for TO equation 1.2 16 CUSUM statistics for GDP equation 16 12 8 4 0-4 -8-12 -16 86 88 90 92 94 96 98 00 02 04 06 08 10 12 1.0 12 0.8 8 0.6 4 0.4 0 0.2-4 0.0-8 -0.2-12 -0.4-16 86 88 90 92 94 96 98 00 02 04 06 08 10 12 86 88 90 92 94 96 98 00 02 04 06 08 10 12 CUSUM of Squares 5% Significance CUSUM 5% Significance CUSUM 5% Significance 1. 6 1.4 CUSUM of SQ GDP equation 1.6 1.2 1.0 0.8 0.6 0.4 0.2 0.0-0.2-0.4 86 88 90 92 94 96 98 00 02 04 06 08 10 12 CUSUM of Squares 5% Significance CUSUM-SQ statistics for TO equation 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0-0.2-0.4 86 88 90 92 94 96 98 00 02 04 06 08 10 12 CUSUM of Squares 5% Significance Note: The straight lines represents critical bounds at 5% significance level. Source: Eviews version 8 1401

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 Grangercausality 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 1402

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. References Abu-Bader, S. and Abu-Qarn, S.A. (2005), Financial Development andeconomic Growth: Time Series Evidence from Egypt, Discussion Paper No. 05-14, Monaster Centre for Economic Research, Ben- Gurion University of the Negev. Al-Malkawi, H. N., & Abdullah, N. (2011). Finance-Growth Nexus: Evidence from a Panel of MENA Countries. International Research Journal of Finance and Economics, 63,129-139. Arouri Mohamed, Gazi Salah Uddin, Kishwar Nawaz, Muhammad Shahbaz, and Frederic Teulon, (2013). Causal Linkages between Financial Development, Trade Openness and Economic Growth: Fresh Evidence from Innovative Accounting Approach in Case of Bangladesh, Ipag Working Paper 2013-037. Atje,R. and Jovanovic,B.,(1993), Finance and Development. Mimeo, New York University. Balassa,B.,(1985), Exports, Policy choices, and Economic Growth in developing contries afeter1973 oil shocks. Journal of Development Economics, 18, pp. 23-35. Banam, K.C. (2010), Impact of Financial Liberalization on Economic Growth in Iran: An Empirical Investigation, Middle Eastern Finance and Economics, Issue 7, 6-37 Baltagi, B.H., Demetriades, P., Law, S.H., 2009. Financial development and openness: Evidence from panel data, Journal of Development Economics 89, 285-296. Calderon, C. and Liu, L. (2003),'The Direction of Causality between Financial Development and Economic Growth', Journal of Development Economics, Vol. 72, No.1, October,321 334. Chimobi Omoke Philip, (2010). The causal Relationship among Financial Development, Trade Openness and Economic Growth in Nigeria, International Journal of Economics and Finance. 2(2), 137-147. Chinn, M.D., Ito, H., 2006. What matters for financial development? Capital controls, institutions and interactions, Journal of Development Economics 81, 163 192. Dickey D.A. and Fuller W.A, (1979) "Distributions of the Estimators for autore-gressive Time Series With a Unit Root", Journal of American Statistical Association, 74, 427-431. Dickey D.A and Fuller W.A, (1981) "Likelihood Ratio Statistics for Autoregressive Time a Unit Root", Econometrica, 49, 1057-1072. Series With Diaz-Alejandro,Carlos,1985, Good-bye financial repression, hello financial crash.journal of Development Economics,19, pp.1-24. 1403

Frankel, J.A., e D. Romer, (1996), Trade and growth: an empirical investigation, NBER Working Paper No. 5476. Fisher, E.O.N., (1995), Growth, Trade and International Transfers, Journal of International Economics 39, 143-158. Siddiki, J.U., (2002). Trade and Financial Liberalisation and Endogenous Growth in Bangladesh. International Economic Journal, Vol.16, 23-37. Soukhakian.B (2007). Financial Development, Trade Openness and Economic Growth in Japan: Evidence from Granger Causality Tests. International Journal of Economic Perspectives, Volume 1, Issue 3, 118-127 Tash, M. N. S., and Sheidaei, Z. (2012). Trade Liberalization, Financial Development and Economic Growth in the Long Term: The Case of Iran. Business and Economic Horizons, 8(2), 33-45. Waqabaca, C., (2004). Financial Development and Economic Growth in Fiji.Working Paper 2004/03 (December).Economics Department, Reserve Bank of Fiji. Hanh P. Thi Hong, (2010). Financial Development, Financial Openness and Trade Openness: New evidence, FIW Working Paper No. 60. Goodhart, C, A, E. (2004),'Financial Development and Growth: Explaining the Links. Palgrave Macmillan. Ghali, K., (1999) "Financial Development and Economic Growth: The Tynisian Experience", Review of Development Economics, 3(3), 310-322. Grossman, G. Helpman, E. (1991),'Quality Ladders in the Theory of Growth', Studies, Vol. 58, No. 1, January, 43 61. Review of Economic Jbili, A. Enders, K. and Treichel, V. (1997), 'Financial Reforms in Algeria, Morocco, and Tunisia: A Preliminary Assessment', IMF Working Paper, July, 97/81. Granger, C.W.J. and Newbold, P. (1986) "Forecasting Economic Time Series'", Academic Press, Inc., New York. Granger, C.W.J, (1988) "Some Recent Developments in a Concept of Causality". Journal of Econometrics, 39, 199-211. Granger, C.W.J. and Newbold, P. (1986) "Forecasting Economic Time Series'", Academic Press, Inc., New York. Granger, C.W.J, (1988) "Some Recent Developments in a Concept of Causality". Journal of Econometrics, 39, 199-211. Ghali, K., (1999) "Financial Development and Economic Growth: The Tynisian Experience", Review of Development Economics, 3(3), 310-322. Greenwood, J. and Jovanovic, B. (1990),'Financial Development, Growth, and the Distribution of Income',Journal of Political Economy, Vol. 98, No. 5, Part 1, October, 1076-1107. 1404