Download the effects of foreign portfolio inventmens on

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 3i  ut
(4)
Here β1, β2, and β3 represent the coefficients of the variables; PRTt 3i 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 31   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. This finding
points out to the importance of the foreign portfolio investments on Turkish economic growth.
On the other hand, the results obtained from this study reveal that foreign portfolio
investment based on hot money is one of the main variables Turkish economic growth. This
mentioned situation will bring with it economy could be fragile against endogenous and
exogenous shocks.
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