Volatility of Foreign Direct Investment and Economic Growth: Empirical Evidence from the Czech...

REsEARCH J ouRNAL OF INTERNATIONAL STUDIES

ISSN: 1453-212X

EDITOR

Adrian Marcus Steinberg Institut fur Interkulturelle S tudien

EDITORIAL AnvisORY BoARD

Awni AlKarzon Northern Illinois University

Wen-jen Hsieh National Cheng Kung University

Nicholas Papasyriopoulos University of Macedonia

Kelechi A. Kalu University of Northern Colorado

Y akin Karatepe Ankara University

Gregorio Gimenez Esteban Universidad de Zaragoza

Jerry Kolo Florida Atlantic University

M. Femi Ayadi University of Houston-Clear Lake

H. Young Baek Nova Southeastern University

George Skoulas University of Macedonia

Mahdi Hadi Kuwait University

Neil Reid University of Toledo

Stylianos Karagiannis Center of P fanning and Economic Research

John Mylonakis Tutor - Hellenic Open University

Athanasios Koulakiotis University of the Aegean

Basel M. Al-Eideh Kuwait University

Narender L. Ahuja Institute for Integrated Learning in Management

Arnita Mital Xavier Labour Relations Institute

W assim Shahin Lebanese American University

Dimitrios Mavridis Technological Educational Institure of West Macedonia

Nicholas Papasyriop.oulos University of Macedonia

Yen Mei Lee Chinese Culture University

Maria Elena Garcia-Ruiz University of Cantabria

© Research Journal of International Studies · 1

Research Journal of International Studies

Indexing-Abstracting

Research Journal of International Studies is indexed by EBSCO, ProQuest ABI/INFORM, SSRN, DOAJ, Elsevier Bibliographic Databases, Ulrich, Cabell's Directory of Publishing Opportunities in Economics and Finance, 10th edition (online edition) and is monitored by Social Sciences Citation Index (SSCI).

Research Journal of International Studies ISSN: 1453-212X Issue 22 (2011) © EuroJoumals Publishing, Inc. 2011 http://www.eurojoumals.com/international_studies.htm

Volatility of Foreign Direct Investment and Economic Growth: Empirical Evidence from the Czech Republic and Hungary

Alpaslan Akcoraoglu Associate Professor, Faculty of Economics and Administrative Sciences

Gazi University, 06500 Besevler, Ankara, Turkey Tel:+ 90 312 216 13 03; Fax:+ 90 312 213 20 36

E-mail: [email protected]

Senay Acikgoz Assistant Professor, Faculty of Economics and Administrative Sciences

Gazi University, 06500 Besevler, Ankara, Turkey E-mail: [email protected]

Atilla Gokce Assistant Professor, Faculty of Economics and Administrative Sciences

Gazi University, 06500 Besevler, Ankara, Turkey E-mail: [email protected]

Abstract The major aim of this paper is to contribute to the empirical literature by presenting timeseries evidence on the relationship between FDI volatility and economic growth in the cases of the Czech Republic and Hungary. The Czech Republic and Hungary are the two major recipient countries of FDI inflows in the Eastern European region. This paper uses EGARCH methodology to model the FDI volatility. Employing the ARDL cointegration approach, the present paper finds that FDI volatility has a negative and statistically significant (long-run) impact on economic growth in these countries in accordance with economic theory. This empirical finding is robust to the choice of the empirical methodology as the results of the Fully-Modified Estimation confirm the significant longrun relationship between FDI volatility and real GDP growth. Furthermore, the empirical results based on ARDL approach indicate that there exists a cointegration or long-run equilibrium relationship between FDI volatility and real GDP growth in the cases of the Czech Republic and Hungary.

Keywords: FDI Volatility, Economic Growth, the Czech Republic, Hungary, ARDL Cointegration, EGARCH Model

JEL Classification Codes: F21, F23, F41, F43

1. Introduction The major aim of this paper is to analyze the empirical relationship between the volatility of FDI .and economic growth in the Czech Republic and Hungary. These countries received large inflows of FDI over the period from 1990 to 2007 and have been two leading recipient countries of FDI inflows in the Eastern Europe. There is a broad consensus among economists on the hypothesis that Eastern

Research Journal of International Studies - Issue 22 (December, 2011) 18

European economies achieved rapid economic growth with recourse to substantial reliance on FDI inflows over this period. On the other hand, the majority of the countries in the region, including the Czech Republic and Hungary, have experienced high volatility of capital flows and reversals associated with volatile flows.

When focusing on the empirical literature on FDI volatility-growth nexus, we find that there exists no rigorous empirical paper which explores the link between volatility of FDI flows and economic growth in the countries of the Eastern Europe. Thus, this paper aims to contribute to the empirical literature by investigating the impact of FDI volatility on economic growth in the cases of Czech Republic and Hungary. Some authors stress that capital inflows to the Eastern European economies are pretty volatile and that most of them have experienced large reversals of capital inflows in their recent economic history (von Hagen and Siedschlag, 2008).

Emerging market economies in general experience greater volatility of capital flows and tend to be more vulnerable to capital flow reversals than advanced economies due to greater financial market failures and imperfections (See, for example, Calvo (1998); Eichengreen et al. (2003); Kaminsky et al. (2004)). The adverse impacts of international capital inflows have gained attention in the literature especially after two major crises experienced by emerging market economies (i.e, Mexican peso crisis of 1994-1995 and East Asian Crisis of 1997-1999). It is often argued that volatility of capital flows causes significant declines in economic growth in emerging market economies, especially in the case of sudden reversals of capital flows with sometimes devastating consequences for the real economy (Calvo and Reinhart (2000); Hutchison and Noy (2002); Edwards (2004)).

The standard economic theory argues that FDI inflows will create new resources for capital accumulation in emerging market economies which have capital shortages. It is also claimed that FDI inflows can improve economic growth and investment levels in recipient countries through the transfer of technology and managerial know-how which accompanies FDI. Although many authors have stressed the significant role of FDI in promoting the economic growth in the Czech Republic and Hungary and other Eastern European economies, rigorous empirical papers on the effects of FDI on economic growth are rather rare in the literature.

In a recent empirical study using dynamic panel approach, Sapienza (2009) investigates the impact of FDI on economic growth in 25 Central and Southern Eastern European (CSEE) countries and finds that FDI has a positive and statistically significant impact on economic growth. Kornecki and Raghavan (2008) analyze how FDI affects the growth of GDP in five Eastern European countries (Poland, the Czech Republic, Hungary, Slovakia and Slovenia) over the period 1993-2003 and find a positive association between FDI stock and economic growth in the analyzed countries using an aggregated growth model based on the production function. In an earlier empirical paper, Campos and Kinoshita (2002) explores the effects of FDI flows on economic growth for 25 transition economies (including some Central and Eastern European countries) and conclude that FDI had a significant positive effect on the economic growth of each country over the period 1990-1998.

The theoretical literature is, however, quite limited on the possible mechanisms that would help to explain the negative association between FDI volatility and economic growth. Some authors emphasize a number of reasons why volatility of FDI flows may have a negative impact on real GDP growth (see, for example, Lensink and Morrisey (2001) and Van Staveren (2011)). First, as the volatility of FDI flows tends to increase uncertainty, the level of investment will decline because of lower expected returns in times of higher volatility (van Staveren, 2011). Most of the empirical studies consistently present evidence that there exists a negative relationship between uncertainty and investment (Goldberg (1993), Aizenman and Marion (1996), Darby et al. (1999), Serven (1998 and 2002)). Since investment is one of the main determinants of economic growth, the increased FDI volatility (and therefore uncertainty) will have a negative impact on growth.

Moreover, Lensink and Morrissey (2001) argue that FDI volatility may reflect economic and political uncertainty in an economy. By economic uncertainty Lensink and Morrissey (2000) refer to "the tendency of some developing countries to be particularly vulnerable to shocks that have the


immediate effect of reducing income and, if recurrent, tend to reduce growth". In addition, they stress that "economic uncertainty is an important determinant of both growth and productivity of investment".

On the other hand, the early neoclassical theory gave little consideration to the role of uncertainty in investment decisions (Bryar, 2008). Only after the theoretical studies of Hartman (1972) and Abel (1983), where they postulated a positive relationship, "the link between uncertainty and investment came to the forefront in the more mainstream literature". More recent work by Dixit and Pindyck (1994) criticized the earlier theory by emphasizing a theoretically negative link between uncertainty and investment.

Second, the recent endogenous growth literature on FDI provides some arguments why FDI volatility may have a negative impact on economic growth (Leusink and Morrissey, 2001). This literature reveals that "FDI positively affects growth by decreasing the costs of R&D through stimulating innovation". "If FDI inflows are uncertain, costs of R&D are uncertain, which negatively affects incentives to innovate". Thus, FDI volatility depresses investment and negatively affects real GDP growth. Furthermore, Leusink and Morrissey (2001) present a simple endogenous growth model in which FDI volatility has a negative impact on growth.

The recent empirical studies suggest that volatility of FDI is associated with lower long-term economic growth (Leusink and Morissey (2002; 2006), Choong and Liew (2009), Guillaumont and Chauvet (2001)). Focusing on instability (volatility) of foreign aids, Leusink and Morrissey (2000) present evidence that aid instability as a proxy for uncertainty retards economic growth in developing countries. Investigating the impact of a measure of the economic vulnerability (a kind of economic uncertainty) in an aid-growth relationship, Guillaumont and Chauvet (1999) find that economic growth is lower in more vulnerable economies, which lends support to the findings of Leusink and Morrissey (2000).

The volatility of FDI flows is widely considered to be smaller than other forms of capital flows. The short-term capital flows are highly volatile and very sensitive to temporary shifts in domestic and international macroeconomic conditions (Gabriele et al. (2000)). Moreover, short-term capital flows are influenced by speculative forces and interest rate differentials. Most of the empirical studies find that FDI is the least volatile and that long-term capital flows are generally more stable than short-term ones (Chuhan et al. (1996), Sarno and Taylor (1999), Razin and Sadka (2007), Felices et al. (2008)). Some empirical studies, however, do not confirm this conclusion (see, for example, Claessens et al. (1995)). Examining whether some forms of international capital flows are more likely to reverse than others, some recent empirical papers find that FDI flows remain very stable during the episodes of sudden stops (Sula and Willet (2006); Levchenko and Mauro (2007)). However, Gabriele et al. (2000) find that all forms of capital flows contributed to the volatility and instability during the 1990s.

This paper explores the impact of FDI volatility on economic growth in the Czech Republic and Hungary by employing the bounds testing (or autoregressive distributed lag (ARDL)) cointegration procedure. The present paper also investigates the robustness of the long-run parameter estimates by examining whether the long-run results of the ARDL estimation confirm the cointegrating vector obtained by the Fully-Modified Ordinary Least Squares (FM-OLS) technique. The fundamental aim of this paper is to contribute to the scarce empirical literature on FDI volatility-growth nexus by presenting time-series evidence for the cases of two major Eastern European countries, namely the Czech Republic and Hungary.

The rest of this paper is organized as follows. Section 2 outlines the data, variables and empirical methodology. Section 3 presents the empirical results. Finally, section 5 summarizes the paper and its conclusions.


2. Data, Variables and Empirical Methodology All data used in this study are taken from International Financial Statistics (IFS). To examine the relationship between FDI volatility and economic growth, we employed quarterly data for the variables in the paper. The data set covers the period 1994Q1-2010Q2 for the Czech Republic and 1995Q1-

2010Q1 for Hungary. The FDI variable is measured by gross FDI. Gross flows of FDI were calculated as Alfaro et al. (2006) indicated. The FDI variable is expressed as a percentage of GDP (FDIGDP). The nominal GDP series are deflated by the GDP deflator in order to construct real GDP series.

In this empirical study, economic growth is represented by the real GDP growth rate (RGR). The real GDP growth rate is approximated by the change in the log of real GDP variable. If ln(RGDP) denotes the natural logarithm of real GDP variable, it can be shown that the rate of change of the real GDP is approximately equal to ln(RGDPt) - ln(RGDPt-i). The time series of real GDP is seasonally adjusted by applying the exponential smoothing. This paper attempts to obtain the volatility of FDIGDP series by employing exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model.

This paper employs Nelson's (1991) EGARCH methodology to model the volatility of FDI in the cases of the Czech Republic and Hungary. Nelson introduced the EGARCH model in which the logarithm of conditional variance is specified as

p Is I q r s log ht= co + I a; ~~~ + I Jj log h+j + Irk ';,;

i=l hc-i j=I k=I hc-k (1)

where h+j denotes the lagged conditional variances of the Et variable while a, 8 and "{ represent the parameters of ARCH, GARCH and leverage parameters respectively. The fact that the EGARCH process is specified in terms of log-volatility implies that ht (cr2

) is always positive and, consequently, there are no restrictions on the sign of the model parameters. The error distribution is Gaussian normal. The present paper employs EGARCH (1, 1) model to estimate conditional variance of the logarithmic gross FDI (FDI/GDP).

This paper explores the relationship between FDI volatility and economic growth in the cases of Czech Republic and Hungary by employing the ARDL approach to cointegration (also known as bounds testing approach). The ARDL was developed by Pesaran and Shin (1999) and Pesaran et al. (2001) as an alternative procedure to the standard cointegration analysis. The ARDL bounds test approach to cointegration involves two stages. In the first stage, an unrestricted error-correction model (UECM) is estimated as follows.

p p

MGR,= a 0 +a1t+a2D, +8iRGRc-1 +52FDI~_1 + LA,jMGR;-j + l::co/l.FDIV,_j +u, (2) j=I j=I

where Dt, 6, and t denote, respectively, a vector of the structural change dummies, the first- difference operator, and a time trend.

The first stage in the ARDL analysis is to estimate equation (2) in order to test for the existence of a long relationship among the variables. The null hypothesis of no cointegration (81 = 82 = 0) against the alternative of a long-run relationship (81 -:/= Dz -:/= 0) is tested by conducting an F-test. Since the asymptotic distribution of this F-statistic is non-standard, Pesaran et al. (2001) calculated two sets of appropriate critical values. One set assumes all variables are I(O) and the other set assumes that they are all I(l). If the calculated F-statistic is higher than the upper bound, there is a cointegration relationship between the series; if it is below than the lower bound, then the variables are not cointegrated, and if it falls within the lower and upper bounds, the result is inconclusive. After a cointegration relationship is established between the variables, the second stage of ARDL approach involves estimating the associated short-run dynamic error-correction model.

It is also of interest to investigate the causality relationships between FDI volatility and real GDP growth. Engle and Granger (1987) show that if the series X and Y are I(l) and cointegrated then there would be a causal relationship at least in one direction. In this paper, tests for Granger causality


are carried out by employing the vector error-correction models (VECM) of long-run cointegrating vectors. These VECMs are given below.

p p

MGR1 =.7l"rn + L7l"11./~YDIV;_j + LTC12,jMGR1_j +r/J,.ECT;_1 +u11 (3) j=I j=I

p p

b.FDI~ = TC20 + L1C21 •1MDI~_1 + L1C22 •1MGR,._1 +(/JiECT;_1 +u21 (4) j=I j=I

In equations (3) and (4), n's are parameters to be estimated, ur's are serially uncorrelated error terms, and ECT1-1 is the error correction term estimated from the cointegrating equation. The Fstatistics on the lagged explanatory variables in these error-correction models indicates the significance of the short-run causal effects. The t-statistics on the coefficients of the lagged ECT terms (<j>j) in equations (3) and ( 4) indicate the significance of the long-run causal effects.

3. Empirical Results In this paper, we employ EGARCH model to estimate the volatility of FDI in the cases of the Czech Republic and Hungary. EGARCH (1, 1) model is specified as follows:

1 h _ !&1-11 s: 1 h £1-1 (S) og t - ffi + a1 -o.s + u1 og -t-1 + "{1 lJ.5 ht-I ht-I

While the mean equation is specified as AR(l) process for the data of the Czech Republic, the mean equation is AR(2) process for the case of Hungary. For our empirical purposes, we prefer to employ AR(2) process in the specification of the mean equation for Hungary. The conditional variance estimates for the FDIGDP variable are presented in Table 1.

Table 1: The Conditional Variance Estimates for FDIGDP

Countries Sample Mean

OJ a1 51 Yi F Period Equation

The Czech 1994Ql-AR(l)

-0.5604* -0.846* 0.7792* 0.3184* 56.5760*

Republic 2010Q2 (-1430.9) (-9.882) (161.913) (3.3796)

Hungary# 1995Ql-

AR(2) 4.2950* -0.1492 0.0587 1.5496*

13.7793* 2010Ql (83.1281) (-0.5294) (1.0312) (8.2863)

Hungary 1995Ql-

ARMA(l,l) 5.5891 * -1.3538* -0.0819* 1.4351 *

34.3744* 2010Ql (72.0719) (-37.157) (-8.0869) (14.5131)

* Indicates the significance at the 1 % level. The t-statistics are provided in parentheses. See Note 4 for EGAR CH (1, 1) model specification.#: The mean equation is specified as AR(2) process for Hungary.

To ensure stationarity, 31 is assumed to be less than one. The Et variable has a normal distribution with a mean of zero and conditional variance (ht). The 31 parameter measures the persistence in conditional volatility. When 31 is relatively large, then volatility takes a long time to die out following a shock. According to our estimations, the persistence effect is approximately 13 times larger in the Czech Republic than Hungary (see Table 1).

The y1 parameter measures the asymmetry or the leverage effect, the importance of this parameter is that the EGARCH model allows us for testing the asymmetries. The estimated coefficients of the standardized lagged residual are positive and statistically significant at 1 % level for the Czech Republic and Hungary. This finding suggests that when there is an unanticipated increase in the gross FDI flows, uncertainty increases more than when there is unanticipated decrease in gross FDI flows. Our conditional variance estimates indicate that this effect is 4.9 times larger in Hungary than the Czech Republic (see Table 1).


This paper investigated the structural breaks for FDI volatility (FDIV) and real GDP growth (RGR) series in the cases of the Czech Republic and Hungary before proceeding to the ARDL analysis. Lee and Strazicich (2003, hereafter LS) proposed an endogenous two break Lagrange-multiplier unit root test that allows for breaks under both the null and alternative hypotheses. This is a basic distinctive property of the LS test. As indicated in Lee and Strazicich (2003), one important issue of the ZivotAndrews (ZA) procedure and other similar endogenous break tests is that they assume no break or breaks under the unit root. Their alternative hypothesis includes the possibility of a unit root with break or breaks. Thus the results obtained from these tests should imply the rejection of a unit root without break or breaks. The results of these two tests accounting for the possible effects of the structural breaks are illustrated in Table 2. The optimal lag length is determined on the basis of the Akaike Information Criterion (AIC).

Table 2: Lee and Strazicich (2003) Unit Root Tests and Structural Breaks

Series LS" LSb

k LM~·stat. TB k LM~-stat. TB

The Czech Republic:

FDIV 0 -3.758* 2000:1 4 -5.215 1998: 1 d,dt(S%)

2002:2d(I%) 2004:1

RGR 6 -2.963 1999:1

5 -4.250 1997: 1

2007:4 2001:3'l,dt(5%; 1%)

Hungary:

FDIV 0 -6.743*** 2000:1 4 -10.764*** 2005:4d,dt(I%)

2002:2d(l%) 2008:3d.dt(S%)

RGR 5 -3.494 2002:1

2 -9.584*** 2007: 1 dt(I%)

2006:4d(S%) 2008:4d(l%)

Notes: a indicates two shifts in level under the unit root null hypothesis. The cntical values are -4.545,-3.842 and -3.504 for l %, 5% and 10% level, respectively. b indicates two changes in level and trend under the unit root null hypothesis. Critical values are -6.41, -5.74 and -5.32 for 1 %, 5% and 10% levels, respectively for A-1 = 0.2 and 'A2 = 0.6. (/.., indicates the location of the breaks.) For details, see Lee and Strazicich (2003: 1084, Table 2). k indicates the number of the lagged augmentation terms by following general to specific procedure and the number of maximum lags is 6. ***, ** and *indicate the null hypothesis of unit root with breaks is rejected at the l %, 5% and 10% levels, respectively. d denotes intercept dummy. dt denotes slope dummy.

The LS test results indicate that the economic growth variable (RGR) is non-stationary for the data of the Czech Republic when structural breaks are taken into account. The results of Lagrange multiplier test in the case of the Czech Republic indicate that FDIV variable is non-stationary when two shifts in level and trend are taken into account. For the data of Hungary, the LM test statistics based on LS procedure show that the volatility of FDI variable (FDIV) is stationary at the 1 % level when structural breaks are taken into account. Considering two shifts in level and trend, the results also indicate that the series of real GDP growth (RGR) is stationary at the 1 % level in the case of Hungary. The time of the structural break (TB) for each variable is also shown in Table 2.

The LS test results reveal that there are some breaks in the volatility of gross FDI flows. Checking the statistical significances of various structural break dummies (including endogenously determined breaks) in the short run (error correction) and long run versions of ARDL procedure, we have used some dummy variables for structural breaks in this empirical paper. In order to account for the impacts of these two structural breaks, the Dt vector is specified to contain the following dummy variables.

These are as follows for the Czech Republic: D97 = 1 for the period 1994Q2-1997Q4, 0 otherwise and D08 = 1 for the period 2008Q4-2009Q3, 0 otherwise. While the first structural break for the data of the Czech Republic represents the period related to the high volatility of capital flows and 1997 currency crisis, the second one corresponds to the effects of global financial and economic crisis


on the economy of the Czech Republic. On the other hand, D08 denotes the dummy variable in the case of Hungary and it takes the value of one for the period 2008Q1-2010Q1. The dummy variable reflects the impact of global crisis on the Hungarian economy and the following recession in the country.

In the first step of the ARDL procedure, we tested for the existence of a long-run relationship between economic growth and volatility of FDI in the cases of the Czech Republic and Hungary. Optimal lag lengths are selected by using AIC. Table 3 presents the F test statistics of ARDL bounds test for cointegration.

Table 3: ARDL Bounds Test Results for Cointegration for the Czech Republic and Hungary

The Critical Values: 90% level 95% level 99% level

I(l) l(O) I(l) l(O) I(l) I(O)

(k = 1, n = 65)The 4.175 4.930 5.130 5.980 7.320 8.435

(4.187) (4.660) (4.950) (5.467) (6.707) (7.360) Czech Republic

[5.755] [6.470] [6.890] [7.660] [9.475] [10.515]

4.145 4.950 5.125 6.000 7.400 8.510 (k = 1, n = 60) Hungary (4.203) (4.693) (4.980) (5.527) (6.780) (7.377)

[5.765] [6.500] [6.905] [7.735] [9.585] [10.420]

Calculated F-statistics and the Lags:

The Czech Republic F(RGR\FDIV) 10.233**• (7.374) *** [10.867] *** Pc=4 p,=4

Hungary F(RGR\FDIV) 15.332*** (10.311)*** [15.108f** Pc= 6 p,=6

Notes: k and n indicate the number of explanatory variables and the number of observations, respectively. The critical and calculated values of F-statistics under the constraint of unrestricted intercept and no trend are presented without any parenthesis and square parenthesis (F-iii).The critical and calculated values of F-statistics under the constraint of unrestricted intercept and restricted trend are presented in parentheses (F-iv). The critical and calculated values of F-statistics under the constraint of unrestricted intercept and unrestricted trend are presented in square parentheses (F-v). The critical values are extracted from Narayan (2005).

This paper uses three variants of the bounds test in Pesaran et al. (2001) when a linear deterministic trend is present. The F-statistic under the constraint of unrestricted intercept and no trend is called F-iii statistic. The F-statistic under the constraint of unrestricted intercept and restricted trend is called F-iv statistic. The F-statistic under the constraint of unrestricted intercept and unrestricted trend is called F-v statistic. The critical values of these F-statistics are extracted from Narayan (2005) because of having small samples.

On the other hand, it should be noted that the unit root test results indicate that the variables of interest can be regarded as stationary in the case of Hungary. Pesaran et al. (2001) suggest that the long-run relationship can be explored by the use of ARDL test procedure when all series are 1(0) in the model. Following this idea, we test whether there is a cointegration relationship between the variables in the case of Hungary or not.

The F test statistics calculated with UECM model were compared with the upper and lower bounds of the critical values in Narayan (2005). As it is seen from Table 3, the estimated F-statistics are higher than the upper bounds of the critical values at the 1 % level in the cases of the Czech Republic and Hungary. It appears that the null hypothesis of no cointegration is rejected, implying the existence of cointegration or long-run equilibrium relationship between economic growth and FDI volatility for the data of these countries

Having shown that there exists a cointegration relationship between the series, this paper proceeds to determine long run and short run relationships. This paper also estimates the long-run relationship by using the FM-OLS technique developed by Phillips and Hansen (1990). The FM-OLS technique is essentially designed to provide optimal estimates of cointegrating regressions. The method


modifies OLS to correct serial correlation effects and to deal with the endogeneity in the regressors that stems from the existence of a cointegrating relationship. Since the FM-OLS technique requires that all series in the system are I(l), it is not appropriate to estimate long run coefficients for the case of Hungary by employing this technique. The estimated long-run coefficients using the ARDL approach and FM-OLS technique are reported in Table 4.

According to the results of ARDL and FM-OLS methodologies, the impact of FDI volatility on the rates of GDP growth of the Czech Republic is negative and statistically significant at the 5% level. The results of ARDL estimation indicate that FDI volatility has a negative and statistically significant impact on economic growth of Hungary over the period 1995Q1-2010Q1• The negative and significant coefficient obtained for the variable of FDI volatility seems to support the theoretical prediction on the relationship between the FDI volatility and economic growth. Thus, the evidence reveals that the increased FDI volatility contributed to the lower rate of economic growth in the Czech Republic and Hungary over the periods under consideration. Furthermore, this result is robust to the choice of estimation methodology for the data of the Czech Republic.

We also report the results of some diagnostic tests in Table 4 and observe that the ARDL models pass these tests against serial correlation, non-normal errors and heteroscedasticity at the 1 % level of significance. The underlying ARDL equation fits well at a highly significant F-test statistic and we can conclude that the ARDL model is correctly specified as indicated by the results of Lagrange Multiplier (LM) test for serial correlation, ARCH(l) test for heteroscedasticity and Jarque-Bera test for normality.

To investigate the stability of the estimated long-run model, we apply the parameter nonconstancy tests for I(l) processes advocated by Hansen (1992b). In his paper, Hansen (1992b) proposes three tests - SupF, MeanF, and Le - which all have the same null hypothesis but differ in their choice of alternative hypothesis. The SupF test is based on the ideas inherent in the classical Chow F-tests. The alternative hypothesis is a sudden shift in regime at an unknown point in time, and amounts to calculating the Chow F-statistic. To perform the SupF test requires truncation of the sample size T. We use the subset [O. IOT, 0.90T]. The MeanF test is appropriate when the question under investigation is whether or not the specified model captures a stable relationship (Hansen, 1992b ). The Le statistic is recommended if the likelihood of parameter variation is relatively constant throughout the sample. Le statistic is also a test of the null of cointegration against the alternative of no cointegration. The test results and their critical values are reported in Table 4.

Table 4: The Estimated Long-run Coefficients

The Czech Republic Hungary

Dependent Estimation Methodology Estimation Methodology

Variable: RGR ARDLa FM-OLS ARDLb ARDL(T)c

0.015 0.0193 0.0158 0.0176 Intercept (4.212)*** (4.216)*** (16.594) *** (8.252) ...

FDIV -0.096 -0.142 -0.00076 -0.00069

(-2.228)** (-2.568) •• (-11.428)*** (-7.541) •••

Diagnostic Checking for ARDL Estimation

Rz 0.526 -- 0.661 0.668

F-stat. 8.418*** -- 7 .284*** 6.715***

0.011 0.265 0.018 AR(l)

[0.915] -- [0.609] [0.891]

2.976 5.790 6.338 ARCH(l)

[0.089] -- [0.020] [0.015]


Table 4: The Estimated Long-run Coefficients - continued

JB-stat. 0.146 0.388 0.462

[0.924] --

[0.823] [0.793] Stability Tests of Hansen (1992a)

With constant With trend With constant With trend SupF 22.873 22.872 35.919 35.544 MeanF 8.987 8.987 21.410 21.217 Le 0.755" 0.755" 0.705" 0.700"

a. b Notes: . The selected number of lags by AIC of the ARDL model IS (4,0). : The selected lags of the ARDL model IS

(3,6). c: ARDL(T) denotes ARDL with a deterministic trend. The t-statistics are given in parentheses. ***, ** and * indicates that the test statistic is significant at the 1, 5 and 10% levels, respectively. AR(l) is the first-order LM test for residual serial correlation. ARCH(l) is the test statistic for autoregressive conditional heteroscedasticity. JBstat. is the Jarque-Bera test statistic for normality test The related p-values are given in square brackets. The SupF, MeanF statistics are calculated using the trimming region [.10, .90]. For stability tests, the null hypothesis is "parameters are stable". The critical values of SupF are 19.0, 15.2 and 13.4 for model with constant at the 1 %, 5% and 10% levels. The critical values of MeanF are 8.61, 6.22 and 5.20 for model with constant at the 1 %, 5% and 10% levels. The critical values of Le are 0.959, 0.623 and 0.497 for model with constant at the I%, 5% and 10% levels. Details can be found in Hansen (1992: 327-328). "shows that the null hypothesis is not rejected at 1% level.

The evidence associated with the Le test statistic establishes the relative constancy of long-run parameters, although the SupF and MeanF test results imply that the estimated parameters are not stable and subject to regime shifts. The result for the Le test indicates that the long-run parameters are relatively constant through time, which also confirms that economic growth maintains a long-run relationship with FDI volatility in the cases of the Czech Republic and Hungary.

In Table 5, we present the results of the short-run dynamic coefficients in the case of the Czech Republic by estimating an ECM associated with the long-run estimates. The results indicate that the coefficients of the error correction terms have the appropriate (negative) signs and are statistically significant at the 1 % level. In particular, the estimated coefficient of ECT1_1 is -0.866 in the model with a constant and implies a very high speed of adjustment to the long-run equilibrium. The lagged error correction term (ECT1_1) in the model with a trend is -0.614 and indicates a high rate of convergence to equilibrium after a shock. Table 5 reports the coefficient estimates of error correction versions of the ARDL models (i.e., short-run coefficient estimates). Since short-run coefficients show only the dynamic adjustment of all series, it is not necessary to attach much interpretation to these coefficients. To check the performance of the estimated error correction model, we also present some diagnostic tests associated with the model (R2

, F-statistic, Durbin-Watson (DW) test statistic for serial correlation).

According to the estimation results of the ECM with trend, the coefficients associated with the lagged differences of the real GDP growth rate (RGR) are statistically significant in the short-run. However, the results of estimation for the VECM indicate that FDI volatility (FDIV) seems to have a statistically insignificant short-run impact on the economic growth. Although the empirical evidence implies a negative and significant long-run relationship between FDI volatility and economic growth in the case of the Czech Republic, it appears that there is no statistically significant relationship between the two variables in the short run.

Table 5: The Czech Republic: Error Correction Representation of the ARDL Model

Dependent Variable: ~RGR AIC/SBC

With constant With trend

~RGR,. 1 -0.085 -0.291

(-0.669) (-3.015)***

~RGR,.2 -0.155 -0.312

(-1.428) (-3.286)"*


Table 5: The Czech Republic: Error Correction Representation of the ARDL Model - continued

ARGRt-3 -0.312 -0.396

(-4.096) *** (-5.645)***

AFDIVt -0.021 0.033

(-0.368) (0.576)

D97 0.002 0.003 (0.252) (0.390)

DOS -0.021 -0.024 (-3.447)*** (-4.103) ***

Intercept 0.00007 0.0002 (0.068) (0.207)

ECTt-1 -0.866 -0.614

(-5.905)*** (-6.263)*** RL 0.794 0.824 F-stat. 34.579*** 30.860*** DW 1.800 2.054 Hansen Stability (1992b)Tests for the ECMs

Joint Le 2.629 2.645

[0.020] [0.020]

Error-variance 0.208 0.229 [0.240] [0.210]

Notes: t-stat1st1cs are given m parentheses. ***, ** and *indicate that the test statistic is significant at the 1 %, 5% and 10% levels, respectively. ECT,_ 1 indicates errorcorrection term. p-values of the Hansen ( 1992a) stability test statistics are given in square parentheses.

Some of the problems of instability could result from insufficient modeling of the short-run dynamics describing departures from the long-run equilibrium (Bahmani-Oskooee (2001)). In view of this, we tested the stability in the ECMs by applying the testing procedure of Hansen (1992a). He describes a simple yet powerful test for testing parameter instability in the dynamic models such as ECMs. We can conduct the procedure of Hansen (1992a) in order to test the stability of all the parameters jointly. Moreover, Hansen (1992a) presents a test statistic for testing the stability of the error-variance. As noted in Hansen (1992a), if a large number of parameters estimated, the joint significance test is a more reliable guide. Following Hansen (1992a), we reported here the stability test results for checking the stability of the error-variance and of all the parameters jointly. The joint stability test (Le) results indicate that the null of parameter stability cannot be rejected at the I% level in all specifications (see Table 5). The similar results were obtained for the error-variance at the standard levels of significance.

Furthermore, this paper explores the causality relationships by implementing the Granger causality tests within the framework of ARDL analysis for the case of the Czech Republic (see Table 6 below). The tests for Granger causality are carried out by the use of the VECM of long-run cointegrating vectors. The results of testing the Granger causality between FDI volatility and the real GDP growth are presented in Table 6. The statistically significant lagged ECT in Table 5 implies that there is a long-run Granger causality relationship from FDI volatility to economic growth in the case of the Czech Republic. However, the hypothesis of non-causality from the FDI volatility to real GDP growth cannot be rejected in the short-run at the standard levels of significance.

Table 6: The Czech Republic: Granger Causality Test Results within the Framework of ARDL Analysis

YIX AFDIV ARGR ECT1_1 (p-values) Without Deterministic Trend

0.586 -1.328 AFDIV --

(0.674) (0.509)

ARGR 0.911 -3.366

(0.465) -- (0.000)


Table 6: The Czech Republic: Granger Causality Test Results within the Framework of ARDL Analysis -continued

With Deterministic Trend

~FDIV 0.914 -0.224

--(0.463) (0.823)

~RGR 1.445 -3.409

(0.232) -- (0.001) Notes: p-values are given m parentheses. H0: Vanable X does not Granger Cause Vanable Y

Finally, this paper provides the causality relationships in the case of Hungary by applying the standard Granger causality tests. The results of testing the Granger causality between real GDP growth and FDI volatility are presented in Table 7. The generated Wald-F test results and the related probabilities are reported in column two and column three. The results presented in Table 7 indicate that empirical model cannot reject Granger causality from FDI volatility to real GDP growth at the I% level.

Table 7: Hungary: Granger Causality Test Results

Null Hypothesis: F-stat. p-value FDIV does not Granger Cause RGR 7.288 0.000 RGR does not Granger Cause FDIV 2.328 0.051

Note: The appropriate lag length is selected by AIC and set to 6.

4. Summary and Conclusion This paper explores the empirical relationship between the FDI volatility and economic growth for the Czech Republic and Hungary by implementing time series analysis unlike most of the previous empirical papers which employ the analysis of panel data. The Czech Republic and Hungary are the two major recipient countries of FDI inflows in the Eastern European region and have attracted significant amounts of FDI inflows since the beginning of the transition process. The FDI inflows have fulfilled an important role as a source of capital in transition from a centrally planned economy to a market-based system. While FDI inflows can improve economic growth in the Czech Republic and Hungary, the high volatility of FDI inflows is predicted to depress real GDP growth in these economies.

Many authors and some recent empirical studies argue that the growth patterns of the major Eastern European countries such as the Czech Republic and Hungary have been mostly driven by FDI inflows especially after the liberalization of their capital accounts in the 1990s. Since economic growth is mostly dependent on FDI inflows, volatility of these inflows may cause serious difficulties in the continuity of GDP growth in these countries. Although FDI is less susceptible to sudden reversals because of its long-term nature, the recent economic history of emerging market economies reveal that it may sometimes respond more like short-term capital flows.

The present paper examined the impact of FDI volatility on real GDP growth for the Czech Republic and Hungary by using the ARDL bounds testing approach. The paper also investigates the robustness of the empirical results by presenting the long-run estimated coefficient of the FDI volatility via the FM-OLS technique in the case of the Czech Republic. The main finding of this paper is that FDI volatility has a negative and statistically significant (long-run) impact on economic growth in accordance with economic theory in the cases of the Czech Republic and Hungary. Furthermore, these empirical findings are robust to the choice of the empirical methodology. Employing ARDL procedure, the paper concluded that there exists a cointegration or long-run equilibrium relationship between FDI volatility and real GDP growth in the same countries.


In conclusion, this paper provides time-series evidence on the FDI volatility-economic growth nexus for the Czech Republic and Hungary by aiming to contribute to the empirical literature.

References [1] Abel, A. 1983. "Optimal Investment under Uncertainty." American Economic Review no.73,

228-233. [2] Aizenman J. and N.P. Marion. 1996. "Volatility and the Investment Response." NBER

Working Papers 5841, National Bureau of Economic Research, Inc. [3] Alfaro, L., S. Kalemli-Ozcan and V. Volosovych. 2006. "Capital Flows in a Globalized World:

The Role of Policies and Institutions", NBER Conference on Capital Controls and Capital Flows in Emerging Economies: Policies, Practices and Consequences, (December 17-18, 2004).

[4] Bahmani-Oskooee, M. 2001. "How Stable Is M2 Money Demand Function in Japan?" Japan and the World Economy, 13(4), 455-461.

[5] Calvo, G.A. 1998. "Capital Flows and Capital-Market Crises: The Simple Economics of Sudden Stops." Journal of Applied Economics, 1(1), 35-54.

[6] Calvo, G. and C. Reinhart 2000. "When Capital Inflows Come to a Sudden Stop: Consequences and Policy Options.", in P B Kenen and A K Swoboda (eds), Reforming the International Monetary and Financial System, International Monetary Fund, 175-201.

[7] Campos, N.F. and Y. Kinoshita. 2002. "Foreign Direct Investment as Technology Transferred: Some Panel Evidence from the Transition Economies." CEPR Discussion Papers 3417.

[8] Choong, C-K. and Liew, V.K.-S. 2009. "Impact of Foreign Direct Investment Volatility on Economic Growth of Asean-5 Countries.", Economics Bulletin. AccessEcon, 29(3), 1829-1841.

[9] Chuhan, P., G. Perez-Quiros and H. Popper. 1996 "International Capital Flows: Do Short-term Investment and Direct Investment Differ?." Policy Research Working Paper no.1507, The World Bank.

[10] Claessens, S. and M.P. Dooley and A. Warner. 1995. "Portfolio Capital Flows: Hot or Cold? The World Economic Review" 9, 153-174

[11] Darby, J. 1999. "The Impact of Exchange Rate Uncertainty on the Level of Investment." Economic Journal, 109, 55-67.

[12] Dixit, A. and R. Pindyck. 1994. Investment Under Uncertainty. (New Jersey: Princeton University Pres, Princeton).

[13] Edwards, S. 2004. "Thirty Years of Current Account Imbalances, Current Account Reversals and Sudden Stops." International Monetary Fund Staff Papers 51(4), 1-49.

[14] Eichengreen, B., R. Hausmann and U. Panizza. 2003. "Currency Mismatches, Debt Intolerance and Original Sin: Why They Are Not the Same and Why It Matters." NEER Working Papers 10036: National Bureau of Economic Research, Inc.

[15] Engle, R.F. and C.W.G. Granger. 1987. "Co-integration and Error Correction Representation, Estimation and Testing." Econometrica, 55, 251-76.

[16] Bryar, D. 2008. The Impact of Capital Inflows on Private Accumulation in Presence of Real Exchange Rate Uncertainty: A Time-Series Analysis of Turkish Experience in the Post-Capital Account Liberalization Period, http://peri.umass.edu/fileadmin!pdf!UM-NSWorkshop/NewSchool2008/Eryar.pdf

[17] Felices, G., G. Hoggarth and V. Madouros. 2008. "Capital Inflows into EMEs Since The Millennium: Risks and the Potential Impact of a Reversal.", Bank of England Quarterly Bulletin, QI.

[18] Gabriele, A., K. Boratav and A. Parikh. 2000. "Instability and Volatility of Capital Flows to Developing Countries" The World Economy, 23(8), 1030-56.


[19] Goldberg, L. 1993. "Exchange Rates and Investment in United States History." Review of Economics and Statistics 75, 575-588.

[20] Guillaumont, P. and L. Chauvet. 1999. "Aid and Performance: A Reassessment.", CERDl,CNRS and University of Auvergne, 1999, mimeo.

[21] Guillaumont, P. and L. Chauvet, 2001. "Aid and Performance: A Reassessment.", Journal of Development Studies, 37: 66-92.

[22] Hartman, R. 1972. "The Effects of Price and Cost Uncertainty on Investment." Journal of Economic Theory, 5: 213-224.

[23] Hutchison, M. and I. Noy. 2002. "Sudden Stops and the Mexican Wave: Currency Crises, Capital Flow Reversals and Output Loss in Emerging Markets." Pacific Basin Working Paper Series 02-03, Federal Reserve Bank of San Francisco.

[24] Kornecki, L. and S. Raghavan. 2008. "The Impact of Foreign Direct Investment on Economic Growth: An Empirical Analysis of Central and Eastern European Countries", European Journal of Management, (Winter).

[25] Hansen, B. E. 1992a. "Testing for Parameter Instability in Linear Models." Journal of Policy Modelling, 14(4), 517-533.

[26] Hansen, B. E. 1992b. "Tests for Parameter Instability in Regressions with I(l) Processes." Journal of Business and Economic Statistics, IO, 321-35.

[27] Kaminsky, G., C. Reinhart and C. Vegh. 2004. "When it Rains, It Pours: Procyclical Capital Flows and Macroeconomic Policies." NBER Working Paper no. 10780.

[28] Lee, J. and M.C. Strazicich. 2003. "Minimum LM Unit Root Test with Two Structural Breaks." The Review of Economics and Statistics, 63, 1082-1089.

[29] Lensink, R. and 0. Morrissey. 2000. "Aid Instability as A Measure of Uncertainty and the Positive Impact of Aid on Growth.", Journal of Development Studies 36, 31-49.

[30] Lensink, R. and 0. Morrissey. 2001. "Foreign Direct Investment: Flows, Volatility and Growth in Developing Countries." Research Report 01El6, University of Groningen, Research Institute SOM (Systems, Organisations and Management).

[31] Len sink, R. and 0. Morrissey. 2006. "Foreign Direct Investment: Flows, Volatility, and the Impact on Growth.", Review of International Economics 14, 478-493.

[32] Levchenko, A. and P. Mauro. 2007. "Do Some Forms of Financial Flows Help Protect Against "Sudden Stops"?." The World Bank Economic Review 21(3), 389-411.

[33] Narayan, P.K. 2005. "The Saving and Investment Nexus for China: Evidence from Cointegration Test." Applied Economics, 37(17), 1979-1990.

[34] Nelson, D.B. 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach." Econometrica 59, 347-370.

[35] Milesi-Ferretti, G.M. and P.R. Lane. 2006. "Capital Flows to Central and Eastern Europe." IMF Working Papers 06, 188, International Monetary Fund.

[36] Pesaran, M.H. and Y. Shin. 1999. "An Autoregressive Distributed Lag Modeling Approach to Cointgeration Analysis", in S. Strom (Ed.), Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium. (Cambridge: Cambridge University Press).

[37] Pesaran, M.H., Y. Shin and R. Smith. 2001. "Bounds Testing Approaches to the Analysis of Level Relationships.", Journal of Applied Econometrics 16, 289-326.

[38] Phillips, P. and B.E. Hansen. 1990. "Statistical Inference in Instrumental Variables Regression with I(l) Processes." Review of Economic Studies, 57, 99-125.

[39] Razin, A. and E. Sadka. 2007. "Productivity and Taxes as Drivers of FDI." NBER Working Papers 13094, National Bureau of Economic Research, Inc.

[40] Sapienza, E. 2009. "FDI and Growth in Central and Southern Eastern Europe" Dipartimento di Scienze Economiche, Matematiche e Statistiche Universita degli Studi di Foggia, Quaderni DSEMS, no.12-2009.


[41] Sarno, L. and M.P. Taylor. 1999. "Hot Money, Accounting Labels and the Permanence of Capital Flows to Developing Countries: An Empirical Investigation." Journal of Development Economics 59(2), 337-364.

[42] Serven, L. 1998. "Macroeconomic Uncertainty and Private Investment in LDCs: An Empirical Investigation", The World Bank, mimeo.

[ 43] Serven, L. 2002. "Real Exchange Rate Uncertainty and Private Investment in Developing Countries." World Bank Policy Research Working Paper no.2823, Washington DC.

[44] Sula, 0. and T.D. Willet. 2006. "Reversibility of Different Types of Capital Flows to Emerging Markets." Munich Personal RePEc Archive, no. 384.

[45] Van Staveren, I. 2011. 'FDI Volatility and Development', in Peter A.G. van Bergeijk, Arjan de Haan and Rolph van der Hoeven (eds), The Financial Crisis and Developing Countries, Cheltenham: Edward Elgar, 101-116.

[46] Von Hagen, J. and J. Siedschlag. 2008. "Managing Capital Flows: Experiences from Central and Eastern Europe." Papers WP234, Economic and Social Research Institute (ESRI).


Volatility of Foreign Direct Investment and Economic Growth: Empirical Evidence from the Czech...

Documents

Transcript of Volatility of Foreign Direct Investment and Economic Growth: Empirical Evidence from the Czech...