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THE EFFECTS OF FOREIGN PORTFOLIO INVENTMENS ON TURKISH ECONOMIC GROWTH: A NONLINEAR APPROACH 1 Asst. Prof. Osman Murat TELATAR1 Karadeniz Technical University, Economy Department, Trabzon/Turkey, [email protected] Abstract Foreign portfolio investments (FPI) are accepted as one of the major component of capital flows. By reason of financial liberalization and globalization process, foreign portfolio investment have become crucial especially for the countries which have the gap of national saving. Although there are numerous studies which investigate the relationship between foreign portfolio investment and economic growth, limited number of studies investigate this relationship by using nonlinear time series analysis. The objective of this study is to examine the impact of foreign portfolio investment on economic growth of Turkey. For this purpose, 1998-2016 period data were considered and nonlinear co-integration test was performed. According to the co-integration and error correction analysis findings, the variables are cointegrated and there is one-way positive relationship from portfolio investment to economic growth. This finding points out the importance of the foreign portfolio investments in economic growth of Turkey. Keywords: Foreign Portfolio Investments, Economic Growth, Turkish Economy, Non-linear Co-integration. 1. Introduction As from 1980s, financial liberalization, removing the obstacles of against capital movements and technological progress have caused the increasing of capital flow. This increasing capital flow has headed to developing countries besides developed countries. International capital flows contribute to economic developments especially for developing countries and meanwhile make fragile to endogenous and exogenous shocks to subjected economies. The reason for this, capital flows seeking to developing countries generally recognise as a portfolio investments. These investments as a named hot money are affected many factors like political uncertainty, the volatility of exchange rate and the changing of interest rates. This case leads to financial crises by the reason of resulting fragile country economies. But however, some countries take measures for removing negative effects of capital flows to preventing spurt of capital flows like Tobin Tax. On the other hand countries which have immediate liquid need, saving gap and largely payments of foreign borrowings, can not perform these delimiting measures. Thanks to mentioned negative effects of portfolio investments, national economies desire capital flows as a foreign direct investments (FDI). Economy of Turkey also has started to attract international capital flows by means of financial liberalizetion activities as from 1980s. Mentioned capital flows generally enter into Turkey in the way of foreign portfolio investments like many other developing countries. The aim of this study is to analyze the effects of foreign portfolio investment on economic growth for Turkey with nonlinear cointegration testing. The second section of study includes the literature which analyzed FPI and GDP relationship. In the third section, econometric method and data set was discussed. In the fourth section, findings obtained as a result of analysis were given. Finally, the concluding remarks were made in the last section. 2. Literature Summary There are numerous studies which investigate relationship between FDI and economic growth in the literature. On the other hand, the number of studies which investigate the relationship between FPI and economic growth is quite few. Moreover, almost there isn’t any study which investigates this relationship by using nonlinear approach. According the result of the studies in literature there is no consensus on the relationship between FPI and economic growth. The results vary by some factors like country group (e.g. develop or developing), period and analyzing method. While some of the studies assert that there is a positive relationship between these two variables, some studies assert that there is a negative relationship between these mentioned variables. Besides, the results of some studies indicate that the relationship between these two variables is meaningless. The literature summary about relationship between FPI and GDP are given in Table 1. Table 1. Literature Summary about Relationship between FPI and GDP Author Period Country Method Durham (2003) 1977-2000 88 Countries Cross Section Regression GDP→FPI (0) Baek (2006) 1989-2002 9 Countries (5:Latin American 4: Asian) Cross Section Regression GDP→FPI (+); Latin American GDP→FPI (0); Asian Duasa and Kassim (2009) 1991-2006 (quarterly data) Malaysia Duasa (2011) 1991-2006 (quarterly data) Malaysia Ekinci (2011) 1996-2008 Rachdi and Saidi (2011) 1990-2009 Yıldız (2012) 1999-2009 Turkey Baghebo and Apere (2014) 1986-2011 Nigeria Mucuk et al. (2014) 1986-2016 Turkey Pala and Orgun(2015) 1998-2012 Turkey Tang (2015) 1987-2012 15 European Union Countries 30 OECD Countries 100 Countries (69:Developing 31: Developed) VAR Granger Causality Toda and Yamamoto Causality Enders and Siklos (2001) Asymmetric Cointegration Test Panel OLS Panel GMM Factor Analysis OLS Johansen Cointegration Test ECM Johansen Cointegration Test VAR Factor Analysis Johansen Cointegration Test OLS Panel OLS Applied Findings GDP→FPI (+) Cointegrated;(M-TAR model) No Cointegration;(TAR model) FPI→GDP (+) FPI→GDP (-); All Countries FDI→GDP (+);Developing FDI→GDP (+); Developed (GDP and Inflation)→FPI (-) Cointegrated FPI→GDP (+) No Cointegration Cointegrated GDP→FPI (+) FPI→GDP (-) Note: Table was formed by authors; (+) reflects positive, (-) negative, (0) meaningless effects in the table, symbols denotes the existence of one-way causality relationship between variables. 3. Method and Data In the literature, all the studies for some exception are based on linear econometric methods. The foreign portfolio investment variable is expected to follow nonlinear process. For that reason, in this study the effects of foreign portfolio investment on economic growth will be investigated by performing nonlinear time series analysis. As an indicator of economic growth, real gross domestic product (GDP) and shares of foreign portfolio investment inflow (PRT) in GDP are included in model which was created with the quarterly data of the period 1998-2016. Both variables is obtained from the database of Central Bank of the Republic of Turkey. The Kapetanios, Shin, and Snell (2003) unit root test were applied primarily for determining whether the variables tracking a nonlinear process. According to this test, while the null hypothesis asserts that the series has unit root, the alternative hypothesis points out a nonlinear ESTAR (exponential smooth transition autoregressive) process (Bahmani-Oskooee and Gelan, 2006: 1). The KSS (2003) unit root test can be formulated as follows (Kapetanios et al., 2003: 361-364): n y t y 3t 1 k y t 1 t (1) k 1 Here yt is the variable, which is tested for unit root and n is the optimal lag length which hasn’t autocorrelation problem. The null and alternative hypotheses of Eq. (1) are as follows: H0: δ=0 H1: δ>0 The t statistic (tNL) which was obtained from δ parameter, enables to test the null and alternative hypotheses. The t statistic is calculated as follows: t NL s.e ( ) (2) Here is ordinary least squares (OLS) estimation result of δ parameter and s.e. denotes standard error. The calculated tNL statistic is compared the critical table value. If the tNL statistic isn’t greater than the critical value, the null hypothesis cannot be rejected. Thus, it can be decided that the relevant series has unit root and a linear process. On the other hand if the tNL statistic is greater than the critical value, the null hypothesis is rejected. So, the relevant series has no unit root and follows a nonlinear process. After applying the nonlinear unit root test, the co-integration relationship between variables was investigated by using the nonlinear co-integration test. The test which was primarily applied by Dufrénot et al. (2006) is similar to the Engle-Granger (1987) cointegration test. The Engle-Granger (1987) co-integration test has two steps. In first step the model is estimated by using OLS method. The residuals obtained from OLS estimation are run the stationary test in next step. At his stage, if the residuals are run the nonlinear unit root test, the nonlinear from of Engle-Granger co-integration test will be carried out. The LSTAR (logic STAR) co-integration model is used in this study is specified as follows: (Dufrénot et al., 2006: 210). z t 01 z t 1 11 z t 1 x t d 31 z t 1 x 3t d t1 (3) where zt is the residuals obtained from the first step of co-integration test; ∆ denotes, the first difference; xt is the independent variable; zt-1xt-d is interaction term and d is the optimal lag selected by using Akaike Information Criteria (AIC). The null hypothesis which is tested to detect validity of co-integration is expressed by this means: H 0 : 11 31 0 (for LSTAR model) For the estimation of the long and short run coefficients of the variables, The nonlinear ARDL approach was used in the study. The nonlinear ARDL approach is consisted from two main steps as linear ARDL analysis. The long run coefficients are obtained by estimating the long run model in first step. The causalities are determined by estimating the short run model in second step. The difference of nonlinear ARDL approach from linear analysis is the nonlinear approach includes nonlinear term in the ARDL model. The ARDL model which is used in the study estimation can be formulated as follows: p q m i 1 i 0 i 0 LGDPt 0 1 LGDPt i 2 PRTt i 3 PRTt 3i ut (4) Here β1, β2, and β3 represent the coefficients of the variables; PRTt 3i denotes the nonlinear term; p, q, and m are the optimal lag length. After estimating the long run coefficients, the study covered error correction model by using Eq. (4). k k i 1 i 0 LGDPt 0 1 ECTt 1 2 ECTt 31 i LGDPt i i PRTt i t (5) where ECT is the residuals obtained from the ARDL model; ECT3 is the nonlinear form of ECT; ∆ denotes the first difference and k is the optimal lag length selected by using AIC. 4. Findings The results of KSS (2003) unit root test are shown in Table 2. Table 2. The Results of KSS (2003) Unit Root Test Variables tNL LGDP PRT Probability %1 %5 %10 -2.186(1) -3.48 -2.93 -2.66 -3.887(1) -3.48 -2.93 -2.66 Note: Number in the parenthesis is the optimal lag order for Akaike Information Criteria (AIC). The asymptotic critical values of tNL statistic are obtained from Table 1 in KSS (2003). L denotes logarithmic form of relevant variable. The results indicate that LGDP is non-stationary [I(1)] which means that the series has a linear process. On the other hand, PRT is stationary [I(0)] and has a nonlinear process. For this reason, the co-integration relationship between variables was analyzed by using nonlinear time series approach. Table 3. The Result of Co-integration Test (for LSTAR process) Model d 01 * Decision LGDP=f(PRT) 5 -0.934a H0 Rejection (co-integrated) Note:* is the coefficient of zt-1 in Equation (1) and d denotes the optimal lag of the interaction term. a; indicates significance at 1% level. Table 3 shows that the results of co-integration test. The result suggests that there is an equilibrium of the long run relationship between the variables. The coefficient of zt-1 is meaningful for statistical at 1% level. At the same time, the null hypothesis is rejected. Hence, it can be easily stated that the two variables are co-integrated. After determining the cointegration between the variables, the study covers nonlinear ARDL model to estimate the long and short run coefficients. The results of ARDL model are given in Table 4. Table 4. The Estimation Results of Nonlinear ARDL (7, 1, 5) Model (Dependent Variable: LGDP) Variables Coefficient t-stat. Constant 0.216 0.516 LGDP(-1) 0.907a 6.784 LGDP(-2) -0.249 -1.387 LGDP(-3) -0.015 -0.098 LGDP(-4) 0.702a 5.672 LGDP(-5) -0.612 a -3.914 LGDP(-6) 0.019 0.113 LGDP(-7) 0.236b 1.998 PRT 0.381 1.447 PRT(-1) 0.507b 1.932 PRT3 -69.939 -0.956 3 PRT (-1) -62.085 -0.828 PRT3(-2) 22.216 0.479 3 PRT (-3) -28.914 -0.626 PRT3(-4) 61.898 1.395 PRT3(-5) -6.300 -0.160 R2=0.98 F=258.78a White: 19.624[0.186] LM*=0.093[0.759] Note: a, and b indicate significance at 1% and 5% level, respectively. * is the BreuschGodfrey LM test statistic for first order autocorrelation. ∆ denotes the first difference. The residuals obtained from the ARDL model was used to estimate the error correction model. The estimation results of error correction model are presented in Table 5. Table 5. The Estimation Results of Error Correction Model (Dependent Variable: LGDP) Variables Coefficient t-stat. Constant 0.010c 1.689 ECT(-1) -0.666b -2.027 ECT (-1) 155.410 1.561 ∆LGDP(-1) 0.244 0.931 3 2 ∆LGDP(-2) -0.419 a -3.794 ∆LGDP(-3) -0.192 -1.297 ∆LGDP(-4) 0.510 a 4.710 ∆LGDP(-5) -0.410a -2.457 ∆PRT 0.205 1.344 ∆PRT(-1) 0.197 1.217 ∆PRT(-2) -0.080 -0.451 ∆PRT(-3) 0.012 0.073 ∆PRT(-4) 0.222 1.383 ∆PRT(-5) -0.328b -2.133 R =0.94 F=61.659 a White=12.592[0.479] LM*=0.685[0.407] Note: a, b and c indicate significance at 1%, 5% and 10% level, respectively. * is the BreuschGodfrey LM test statistic for first order autocorrelation. ∆ denotes the first difference. As it is seen in Table 5, the lag of error correction term [ECT(-1)] is negative and meaningful statistically as expected. This result supports the findings of the nonlinear cointegration test. Hence, the ECT(-1) shows that a deviation from current period equilibrium with the amount of 66% has been eliminated in a following period. More clearly, the ECT(-1) value (0,666) means that when the system is exposed to a shock, converging the long run equilibrium takes nearly one and a half periods. Besides, the sum of PRT coefficients have positive sign which means that the foreign portfolio investment has positive effect on economic growth. 5. Concluding remarks This study aims to determine the effects of foreign portfolio investment in economic growth of Turkey. In this sense, it was applied nonlinear co-integration test by using quarterly data which covers 1998:01-2016:01 period. According to co-integration test, the variables are co-integrated. Besides, the estimation results of error correction model indicate that there is a positive effect from FPI to GDP. In other words, an increase of the foreign portfolio investment inflow causes to grow Turkish economy. On the other hand, a decrease of the foreign portfolio investment deteriorates the economic growth performance. 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