Exports and Economic Growth of Turkey: Co-integration and Error-Correction Analysis

Transcription

1 Expors and Economic Growh of Turkey: Co-inegraion and Error-Correcion Analysis Mura KARAGÖZ * Ali ŞEN ABSTRACT: This paper employs modern economeric ime series mehods such as coinegraion and error-correcion o analyze he dynamic relaionship beween expor growh and economic growh in Turkey, using quarerly daa from 980 o From he heoreical poin of view, he expor growh should conribue posiively o economic growh. In fac, his poin is he raional behind he expor-led growh hypohesis. The empirical research conduced here shows ha a uni-direcional long erm causaliy exiss from expor growh o economic growh in Turkish Economy. In erms of error correcion models, here is evidence for shor-run Granger causaliy running from expor growh o economic growh. However, here is as well evidence for shor-run causaliy running from economic growh o expor growh. KEY WORDS and PHRASES: Causaliy, Expor-led Growh, Coinegraion, Error Correcion, Turkish Economy. ÖZET: Bu makalede 980 den 2004 e kadar çeyrek yıllık verilerle Türkiye de ihraca ile ikisadi büyüme arasındaki dinamik ilişki koenegrasyon ve haa düzelme gibi modern ekonomerik yönemler kullanılarak analiz edilmekedir. Teorik olarak ihracaın büyümeye olumlu eki emesi gerekir. İhraca öncülüğünde büyüme hipoezi manığı da buna dayanmakadır. Burada gerçekleşirilen amprik araşırma, Türkiye de ihracaaki büyümeden ekonomik büyümeye ek yönlü uzun dönem nedenselliği olduğunu gösermekedir. Haa düzelme modellerinde ise hem ihracaan büyümeye hem de büyümeden ihracaa kısa dönemli nedensellik gözlenmişir. ANAHTAR KELİME ve DEYİMLER: Nedensellik, İhraca kaynaklı büyüme, Koenegrasyon, Haa düzelme, Türk ekonomisi. Inroducion The hypohesis of uni-direcional causaliy from expor growh o economic growh is popularly known as expor-led growh hypohesis (ELGH for shor). There is a growing body of lieraure examining he EGLH in developing counries boh in heoreical and empirical erms. According o Ekanayake (999) hese sudies can be classified in four main groups boh from hisorical and mehodological poin of view. The early sudies regarding he ELGH, examined he simple correlaion coefficien beween expor growh and economic growh. Among hese sudies Michaely (977), Balassa (978), and Kormendi and Mequire (985) are couned. These sudies are generally based on some descripive saisics and concluded ha here is srong evidence in favor of ELGH depending on he fac ha expor growh and economic growh are highly correlaed. The main * Assoc.Prof., Inönü Universiy, Deparmen of Economerics, mkaragoz@inonu.edu.r Assis.Prof., Inönü Universiy, Deparmen of Economics, asen@inonu.edu.r

2 weakness of his group of sudies is ha a high degree of posiive correlaion beween he wo variables was used as evidence supporing he ELGH. The second group of sudies ook he approach of wheher or no expors are driving oupu by esimaing oupu growh regression equaions based on he neoclassical growh accouning echniques of producion funcion analysis, augmened by expors or expor growh as an explanaory variable. To menion bu a few of he sudies done on his line include Balassa (985), Lucas (990), and Sprou and Weaver (993). In his group of sudies, a highly significan posiive value of he coefficien of expor growh variable in he growh accouning equaion and a significan improvemen in he coefficien of deerminaion wih he inclusion of he expor growh variable in he regression equaion are shown as evidences for ELGH. As poined ou by Ekanayake (999) his group of models criicized for making a priori assumpion ha expor growh causes oupu growh and do no consider he possibiliy of bi-direcional causaliy beween he wo variables. The hird group of sudies makes a srong emphasis on causaliy beween expor growh and economic growh. This approach has been aken in Jung and Marshall (985), Bahmani-Oskooee e al. (99), and Holman and Graves (995) and designed o assess wheher or no individual counries exhibi evidence for ELGH using Granger or Sims causaliy ess. The major drawback of hese sudies is ha he Granger or Sims ess used in hese sudies are only valid if he original ime series are coinegraed. Therefore, coinegraing properies of original expor and oupu series should be checked firs before using Granger or Sims ess. The final group of sudies, ha has been relaively new which involve he applicaion of echniques of coinegraion and error-correcion models includes Bahmani-Oskooee and Alse (993), Sengupa and Espana (994), Ghaak, Milner and Ukulu (997), Ekanayake (999), Richards (200), Ngoc e al (2003). The presen sudy as well can be couned as a member of his las group. The aim of his sudy is o invesigae he dynamic relaionship beween expor growh and economic growh (in erms of oupu growh) in Turkish economy using coinegraion and errorcorrecion models. There are already several sudies ha employed his mehodology o invesigae he dynamic relaionship beween expor growh and economic growh in developing economies. 2

3 Considering he relaive scarciy of using his mehodology in empirical invesigaion of exporgrowh relaionship, we hope ha his sudy will make a modes bu cerain conribuion o he empirical lieraure. The main body of he sudy is organized as follows. The mehodology of coinegraion and errorcorrecion models is presened in secion 2. The sources and properies of daa and he empirical resuls are repored in secion 3. In he final secion of 4, a discussion of implicaions of resuls and some summary conclusions are presened. 2. A Brief Accoun of Employed Mehodology This sudy employs relaively new mehods of ime series economerics, such as coinegraion and error-correcion models, o es he dynamic relaionship beween expors and economic growh. The populariy of hese mehods in recen empirical research depend on a number of reasons among which he simpliciy and relevance in analyzing ime-series daa of paramoun imporance. Moreover hey ensure saionariy and furher possibiliies hrough which Granger-causaliy could be invesigaed in he face of coinegraion. Alhough hese echniques are widely used in a VAR (vecor auoregressive) conex, here we use hese mehods in a bivariae modeling framework. Granger (969) developed a es o check wheher or no he inclusion of pas values of a variable X improves he predicion of presen values of variabley. If he predicion of Y is improved by including pas values of X relaive o only using he pas values of Y, hen X is said o Granger-cause Y. In he same manner, if he pas values of Y improve he predicion of X relaive o using only he pas values of X, hen Y is said o Granger-cause X. If boh X is found o Granger-cause Y and Y is found o Granger-cause X, hen here is said a feedback relaionship. Ye here is a possibiliy of spurious causaliy. To avoid i, boh series need o be saionary. The propery of non-spurious longrun equilibrium (saionary) relaionship among economic variables is referred o in he lieraure as coinegraion. Granger (988) assers ha, sandard ess for causaliy are valid only if here exis a coinegraing relaionship. Tha is, o check he coinegraing properies of he variables under consideraion is a necessary precondiion for causaliy esing. The coinegraion and error-correcion mehodology is briefly oulined as follows. Granger (986), Engle and Granger (987), have invesigaed he causal relaionship beween wo variables when a 3

4 common rend exis beween hem. A non-saionary ime series Y is said o be inegraed of order d, if is saionariy is achieved afer being differenced d imes. Equilibrium heories regarding he non-saionary variables require a linear combinaion of he variables o be saionary. The deviaions from equilibrium mus be emporary. If wo series Y and X are boh I( d ), a linear combinaion, be coinegraed. Y ax = u, is inegraed of order ( d b ), and b > 0, hen Y and X are said o Coinegraion es beween he wo variables can be done in four seps (Enders, 2004, p.335). Firs, prees he ime series for heir order of inegraion. The number of uni roos in each variable should be deermined by performing he augmened Dickey-Fuller (ADF) es. The ADF es is based on he regression equaion wih he inclusion of a consan and a rend of he form Y = α + α + β Y + 0 p + γ j Y j ε, () j= where Y = Y Y and Y is he variable under consideraion, p is he number of lags in he dependen variable, is chosen so as o induce a whie noise erm and ε is he sochasic error erm. The saionariy of he variable is esed using he null hypohesis of β = 0 agains he alernaive hypohesis of β < 0. Rejec he null hypohesis if he es saisic is less han he criical value in real erms. If he null hypohesis canno be rejeced, i implies ha he ime series is non-saionary a he level and herefore i requires aking firs or higher order differencing of he level daa o esablish saionariy. As i is obvious from he alernaive, ADF is a one-sided es and one can use hree ypes of ADF regression of (), ha is, inercep and/or deerminisic ime rend can ake place or no. Engle and Granger (987) prefer he ADF es due o he sabiliy of is criical values as well as is power over differen sampling experimens. The opimum lag lengh in he ADF regression insures he residuals no o be serially correlaed. To have a reliable es resul, all he coefficiens in he regression mus be significan and residuals should imiae a whie noise process. Sandard ime series economeric mehodologies assume saionariy in he variables. Oherwise he usual saisical ess are inappropriae and he inferences drawn will be misleading. As Granger and Newbold (974) righfully poined ou, he ordinary leas squares (OLS) esimaion of regressions 4

5 for example, in presence of non-saionary variables give rise o spurious regressions if he variables are no coinegraed. Therefore esing he economic ime series for saionariy is of grea imporance. Having esed he saionariy of each ime series, and confirmed ha each series have he same order of homogeneiy (d), he nex sep is o search for coinegraion beween X and Y. In his sep we invesigae wheher here is a long run relaionship beween he sochasic rends of X and Y. In order ha X and Y have any ype of causaliy, hey mus be coinegraed in he Granger sense. This precondiion can be confirmed by using eiher he Engle-Granger wo-sep coinegraion procedure or Johansen-Juselius rank-based coinegraion es. The Engle-Granger procedure involves wo seps. In he firs sep, for an economic model based on wo ime series Y and X, saionariy of each variable are examined by uni roo ess. Following he saionariy ess, if he wo series have he same order of inegraion, hen eiher one coinegraing regression which is a linear combinaion of he series or wo coinegraion regressions (direc and reverse) beween he wo variables can be esimaed using he OLS. Direc and reverse regressions are obained by normalizing for respecive seleced dependen variables. The second sep involves direcly esing he saionariy of error processes of coinegraion regressions esimaed in previous sep. In he hird sep he error correcion models are esimaed. If he variables are coinegraed here mus exis an error-correcion represenaion ha may ake he following form: k Y = α + β e + γ Y + γ Y + ε, (2) i i= i k i= 2i 2 i 2 2 k k 2e + δi Y i + δ 2i Y2 i ε 2, (3) i= i= Y = α + β + where e is he residuals of, or discrepancies from he, long-run (coinegraing) relaionship and β and β 2 are he error-correcion coefficiens. The inclusion of error-correcion erms in equaions (4) and (5) inroduces an addiional channel hrough which Granger causaliy could be deeced. According o Granger (986), he error-correcion models produce beer shor-run forecass and provide he shor-run dynamics necessary o obain long-run equilibrium. However, in he absence of coinegraion, a vecor auoregression (VAR) in firs-differences form can be consruced. In his 5

6 case, he error-correcion erms will be eliminaed from equaions (4) and (5). If he series are coinegraed, hen he error-correcion models given in equaions (4) and (5) are valid and he coefficiens β and β 2 are expeced o capure he adjusmens of Y and Y 2 owards long-run equilibrium, while Y i and Y2 i are expeced o capure he shor-run dynamics of he model. I migh be he case ha while one regression residuals are saionary he oher one may no be. The es for coinegraion should be robus o he choice of he variable seleced for normalizaion. In he case of hree or more variables, here may be more han one coinegraing regression. Bu mos imporan defec of Engle-Granger procedure is is reliance on wo-sep esimaion. Forunaely, Johansen (988), and Johansen and Juselius (990) have developed a maximum likelihood esing procedure on he number of coinegraing vecors which also include esing procedures for linear resricions on he coinegraing parameers, for any se of variables. Two es saisics ha are used o idenify he number of coinegraing vecors, namely he race es saisic and he maximum eigenvalue es saisic, are given here. For he null hypohesis ha here are a mos r disinc coinegraing vecors, he es saisic is p λ race ( r ) = T ln( λ j ), (4) j= r+ where λ j s are he p r smalles squared canonical correlaions beween Y k and Y (where Y = (Y,Y 2 ) and where all variables enering Y are assumed o be I( ), correced for he effecs of he lagged differences of he Y process. The maximum likelihood raio or pu anoher way, he maximum eigenvalue saisic, for esing he null hypohesis of a mos agains he alernaive hypohesis of r + coinegraing vecors, is given by r coinegraing vecors λ r ) = T ln( λ ) (5) max ( r+ Some economeric sofware may no produce his las saisics, bu i can be calculaed by he firs one as follows, 6

7 λ r ) = λ ( r ) λ ( r ) (6) max ( race race + Johansen (988) argues ha, λ race and λ max saisics have non-sandard disribuions under he null hypohesis, and provides approximae criical values for he saisic, generaed by Mone Carlo mehods. 3. Daa and Empirical Resuls Quarerly daa for he period 989Q-2004Q4 were used for esimaion. The daa on expors and gross naional produc (GDP) for Turkey are obained from CBRT websie LNRGDP LNREXP Figure. Seasonally Adjused Logarihmic Real GDP and Expor Series As a precondiion of he employed mehodologies, saionariy of he series are examined and he empirical resuls are discussed in his secion. In Table we presen he resuls of uni roo ess obained using he augmened Dickey-Fuller es. The resuls are based on quarerly series of expor and GDP for Turkey. The span of 989Q-2004Q4 reflecs daa availabiliy. Table. Augmened Dickey-Fuller Uni Roo Tess 7

8 Dependen Variable Number of Lags Deerminisic Regressors 5 % Criical Value ADF Tes Saisic Durbin- Wason sa Prob (F-saisic) D(LNREXP) 0 none D(LNREXP) 2 none D(LNREXP) 2 inercep D(LNREXP) 0 rend + in D(LNREXP) 2 rend + in D(LNREXP,2) 0 inercep D(LNRGDP) 0 none * D(LNRGDP,2) 0 none Noes: All he firs difference ADF regressions have a significan uni roo coefficien a he 5% levels, a DW saisic nearing o he value of 2 means no auocorrelaion in ADF regression. A significan F saisics shows he overall performance of ADF regression. * shows ha his ADF regression is significan only a 0 % level. The resuls poins o he presence of uni roos in boh series. More specifically, he null hypohesis ha he series are non-saionary is no rejeced a he levels of boh variables. However, when he firs differences of he variables are considered, he null hypohesis is rejeced in favor of alernaive hypohesis which sae ha he series are saionary. Thus, heir firs difference is found o be saionary and hence LNREXP and LNRGDP are boh inegraed of order one, I( ). The nex sep involves applying Engle-Granger wo-sage coinegraion procedure and Johansen- Juselius coinegraion es o check wheher he wo variables are coinegraed. The opimum lag lenghs are deermined using he Akaike final predicion error (FPE) crierion. The resuls of he ADF es applied o residuals of he coinegraion equaions are presened in Table 2. The resuls indicae ha he esimaed ADF saisics for he residuals are greaer han heir corresponding criical values for boh series. Table 2. Resuls of Engle-Granger Tes Coinegraing Equaion slope s. error -values ADF for Residuals LNREXP=f(LNRGDP) * LNRGDP=f(LNREXP) * Noes: * indicae he saisical significance a he % level of significance. % criical values of ADF saisic for residuals is Sample period: 989: 2004:4, Included observaions: 64 8

9 The residuals of coinegraing equaions are ploed in Figures 2 and 3. From he inspecion of hese figures i is obvious ha hese wo residuals are symmerical and corresponding regressions are in fac idenical Residual Acual Fied Figure 2. Residual Plos For he Coinegraing Equaion LNREXP=f(LNRGDP) Residual Acual Fied Figure 3. Residual Plos For he Coinegraing Equaion LNRGDP=f(LNREXP) Secondly, he Johansen-Juselius coinegraion es has been performed for his wo series and he resuls of his es which has been presened in Table 3 below, also provide evidence for he exisence of one coinegraion vecor implying ha he wo variables are coinegraed. Table 3. Johansen Coinegraion Tess Resuls Eigenvalue Likelihood Raio 5 Percen Criical Value Percen Criical Value Hypohesized No. of CE(s) 9

10 None * A mos Unnormalized Coin. Coefficiens Normalized Coinegraing Coefficiens: LNRGDP LNREXP LNRGDP LNREXP C (0.8265) ( ) ( ) Noes: Sample period: 989: 2004:4, Included observaions: 6, Tes assumpion: Linear deerminisic rend in he daa, Lags inerval: o 2, * denoes rejecion of he hypohesis a % significance level, Sandard errors are in parenheses. From Table 3 above, we see ha he likelihood raio es indicaes coinegraing equaion a 5 % significance level. This resul confirms he Engle-Granger wo sage es for coinegraion. Thus, he resuls of boh Engle-Granger wo-sep procedure and Johansen-Juselius coinegraion es imply a long-run associaion beween real expors and real GDP series for Turkey. Therefore, equaions (2) and (3) have been esimaed including he error-correcion erms. The empirical resuls of he esimaed error-correcion models are presened in Table 4. The resuls show ha bi-direcional causaliy exiss beween expor growh and GDP growh. This is based on he saisical significance of he error-correcion coefficiens ( and b ) of he error-correcion (EC) erms. The error-correcion erms e b 2 represens he long-run impac of one variable on he oher while he changes of he lagged independen variable describe he shor-run causal impac. The resuls presened in Table 4 provide evidence on long-run impac from expor growh o economic growh as well as from economic growh o expor growh. The shor-run dynamics of he errorcorrecion processes can be idenified by examining he saisical significance of he values given in hese wo columns. The opimum lag lenghs for auoregressive erms in equaions (2) and (3) were idenified using he Akaike final predicion error crierion. The saisically significan non-zero coefficiens show ha he shor-run Granger causaliy runs from GDP growh o expor growh. Similarly, he saisically significan non-zero coefficiens reflec feedback beween curren changes in real expors and is own lagged values. Furher, he resuls presened in he boom par of Table 4 indicae ha he non-zero coefficiens reflec feedback beween curren changes in real GDP and is own lagged values. The saisically significan non-zero coefficien show ha he shor-run Granger causaliy runs from expor growh o GDP growh. 0

11 Table 4. Esimaion Resuls of Error Correcion Model Error Correcion: D(LNRGDP) D(LNREXP) Variables Coefficiens S. Error -Values Coefficiens S. Error -Values C ( ) ( ) (0.0032) (4.8205) EC ( ) ( ) (0.7337) ( ) D(LNRGDP(-)) (0.098) (0.8058)* ( ) ( ) D(LNRGDP(-2)) (0.942) (0.5664)* ( ) ( )* D(LNREXP(-)) (0.0656) (-4.825) (0.2805) ( ) D(LNREXP(-2)) (0.0609) ( ) (0.2707) ( ) Noes: Sample(adjused): 989:4 2004:4, Included observaions: 6 afer adjusing endpoins. EC denoes he error-correcion erm and criical values for which is a he 5% level of significance. * These values are no significan a 5% significance level. Error-correcion resuls of Table 4 shows ha in boh equaions he error correcion erms are significan. The oher coefficiens are significan in general significan. All hese resuls confirms ha, beside of long-erm, here is a significan shor erm relaionship as well beween expor and growh. Table 5. Pairwise Granger Causaliy Tess Resuls Null Hypohesis: Obs F-Saisic Probabiliy LNREXP does no Granger Cause LNRGDP LNRGDP does no Granger Cause LNREXP Noes: Sample: 989: 2004:4, Number of Lags: 4. In Table 5 we presen Granger causaliy es resul. As i is obvious from he able, here is a significan Granger-causaliy from expor o growh, bu he reverse is no significan. This resul confirms ha here is no feedback relaionship beween hese wo variables. Since we have esablished he direcion of he long-run (equilibrium) relaionship, in Table 6 below we have ried several equilibrium models. Table 6. Long-run Relaionships from LNREXP o LNRGDP Model Variable Coefficien Sd. Error -Saisic Prob. C LNREXP

12 R-squared Mean dependen var Adjused R-squared S.D. dependen var S.E. of regression Akaike info crierion Sum squared resid Schwarz crierion Log likelihood F-saisic Durbin-Wason sa Prob(F-saisic) Model 2 Variable Coefficien Sd. Error -Saisic Prob. C LNREXP TREND R-squared Mean dependen var Adjused R-squared S.D. dependen var S.E. of regression Akaike info crierion Sum squared resid Schwarz crierion Log likelihood F-saisic Durbin-Wason sa Prob(F-saisic) Model 3 Variable Coefficien Sd. Error -Saisic Prob. C LNREXP AR() R-squared Mean dependen var Adjused R-squared S.D. dependen var S.E. of regression Akaike info crierion Sum squared resid Schwarz crierion Log likelihood F-saisic Durbin-Wason sa Prob(F-saisic) Among he alernaives Model 3 seems o be he bes one in erms of boh coefficiens of deerminaion (R-squared) and AIC (Akaike s Informaion Crieria). The residuals plo of his final model is presened in Figure 4 below. The plo of residuals imiaes a whie noise process. In fac he ADF es of residuals has shown no sign of uni roos. 2

13 Residual Acual Fied Figure 4. Residuals of Model Conclusions This sudy uses ime series economeric ools such as causaliy, coinegraion and error-correcion models o invesigae he dynamic relaionship beween expor growh and economic growh in Turkish economy. The applied economics lieraure regarding ELGH sudies have reached mixed resuls for differen economies. These sudies employed simple descripive saisics, Grangercausaliy, Coinegraion and error correcion mehodologies. The coinegraion modeling echniques used in his paper have revealed ha here is a unidirecional causaliy from expor growh o economic growh in Turkey. There is evidence for longrun Granger causaliy running from economic growh o expor growh in Turkey. Error-correcion analysis confirms bi-direcional shor-run relaionship, ha is, gives evidence for shor-run Granger causaliy running from expor growh o economic growh, and evidence of shor-run causaliy running from economic growh o expor growh. References Bahmani-Oskooee, M., H. Mohadi, and G. Shabsigh (99), Expor Growh and Causaliy in LDCs: A Reexaminaion, Journal of Developmen Economics, Vol. 36, Bahmani-Oskooee, M., and J. Alse (993), Expor Growh and Economic Growh: An Applicaion of Coinegraion and Error-Correcion Modeling, Journal of Developing 3

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15 Economic Review, 80, Michaely, M. (977), Expors and Growh: An Empirical Invesigaion, Journal of Developmen Economics, Vol. 4, Ngoc, P. M., N. T. P. Anh, and P. T. Nga. (2003) Expors and Long-run Growh in Vienam, , ASEAN Economic Bullein. Singapore:Vol.20, Iss. 3; pg. 2, 22 pgs. Richards, D.G. (200), Expors as a Deerminan of Long-Run Growh in Paraguay, , The Journal of Developmen Sudies, Vol.38, No., pp Sengupa, J.K., and J.R. Espana (994), Expors and Economic Growh in Asian NICs: An Economeric Analysis for Korea, Applied Economics, Vol. 26, 4-5. Sprou, R.V.A., and J.H. Weaver (993), Expors and Economic Growh in a Simulaneous Equaions Model, Journal of Developing Areas, Vol. 27, No. 3,