Time-varying linkages beween Tourism Receips and Economic Growh in Souh Africa Mehme Balcilar a, Reneé van Eyden b,*, Roula Inglesi-Loz b and Rangan Gupa b Absrac The causal link beween ourism receips and GDP has recenly become a major focus in he ourism economics lieraure. Resuls obained in recen sudies abou he causal link appear o be sensiive wih respec o he counries analysed, sample period and mehodology employed. Considering he sensiiviy of he causal link, we use rolling window and ime-varying coefficien esimaion mehods o analyse he parameer sabiliy and Granger causaliy based on a vecor error correcion model (VECM). When applied o Souh Africa for he 1960-2011 period, he findings are as follows: resuls from he full sample VECM indicae ha here is no Granger-causaliy beween ourism receips and GDP, while he findings from he ime-varying coefficiens model based on he sae-space represenaion show ha ourism receips have posiive-predicive conen for GDP for he enire period, wih he excepion of he period beween 1985 and 1990. Full sample ime varying causaliy ess show bidirecional srong causaliy beween ourism receips and GDP. Keywords: Tourism receips; economic growh; ime-varying causaliy; ime-varying parameer model JEL codes: C32, L83, O40 a Deparmen of Economics, Easern Medierranean Universiy, Famagusa TR, Norhern Cyprus, via Mersin 10, Turkey. b Deparmen of Economics, Universiy of Preoria, Privae Bag X20, Hafield, 0028,Preoria, Souh Africa. * Corresponding auhor. Email: renee.vaneyden@up.ac.za. 1
1. Inroducion Over he pas six decades, he ourism secor experienced coninued expansion and diversificaion, becoming one of he larges and fases-growing economic secors globally. I is he larges service secor indusry in erms of world rade, accouning for 6 per cen of world expors and conribuing abou 9 per cen o world GDP (UNWTO, 2013). The imporance of he ourism secor lies wih he fac ha i is a labour-inensive secor wih he poenial o creae jobs for relaively unskilled workers. I is also an imporan earner of foreign exchange world ourism receips oalled US$ 1,075 billion in 2012. When considering he fac ha 35.9 per cen of his amoun has been received by emerging economies, he relaive imporance of he ourism secor in lower income counries is eviden. For Souh Africa in paricular, ourism receips amouned o US$ 9,994 million or 0.92 per cen of he world oal. The imporance of he secor for he Souh African economy has also increased. Before 1994, ourism receips consiued an average of 2.1 per cen of GDP, while his figure increased o an average of 3.6 per cen of GDP in he pos-sancion period, wih a conribuion o GDP as high as 4.9 per cen in 2003. Wihin Africa, Souh Africa has he larges marke share on he coninen, namely 30 per cen. I is widely suggesed in he lieraure (e.g. Vanegas and Croes, 2003; Theobald, 2001) ha economic growh creaed by ourism receips in a counry sems from he relaionship beween expors and economic growh. Specifically, he role of such economic aciviy, as a promoer of shor and long-run economic growh, is invesigaed by assessing he so-called Tourism Led Growh Hypohesis (TLGH), which is direcly derived from he Expor-Led Growh Hypohesis (ELGH). The laer posulaes ha economic growh can be generaed, no only by increasing he amoun of labour and capial wihin he economy, bu also by expanding expors. Since ourism receips are considered par of inernaional services and have a foreign exchange naure, i can be argued ha ourism receips have an expor effec (Arslanurk, Balcilar and Ozdemir, 2011). Theobald (2001) also characerizes ourism as an inangible expor iem. The ourism secor involves a variey of services and professions from oher secors, including: accommodaion services, food and beverages, ransporaion services (passenger ransporaion and ranspor equipmen renal), culural services, spors and recreaional services, ravel services (ravel agencies and oher reservaion services), as well as counry-specific ourism characerisic goods and services (including reail rade) (UNWTO, 2010). The demand for goods and services in he hos counry will be increased wih he rise in ouris arrivals (Dieke, 2000). If he economy visied has he resources o mee he increasing demand as a resul of he number of ouriss, he spending will remain in he counry visied. Foreign exchange surpluses creaed by ourism aciviies will have a posiive effec on he balance of paymens, which is why i is widely acknowledged ha ourism in he long run migh creae economic growh (Balaguer and Jorda, 2002; Croes, 2006). In addiion o having a posiive impac on he curren accoun, growh in he ourism secor also encourages invesmens in new infrasrucure, simulaes aciviy in relaed economic indusries, 2
creaes employmen and induces an increase in GDP, hus having a desired effec on he economy concerned (Brohman, 1996; Brida and Pulina, 2010). The causaliy relaionship beween expors and economic growh has been he focus of researchers for a long ime (e.g. Balassa, 1988; Ghaak e al. 1997). More recenly he causal link beween ourism receips and economic growh, or income, has sared o receive a grea deal of aenion in ourism economics. The firs sudy o es he TLGH was by Balaguer and Canavella-Jordà (2002) for Spain. A large number of oher sudies followed, of which he greaer majoriy propose a vecor error correcion model (VECM) framework. Oher ime-series echniques employed include he Sock and Wason (1993) dynamic ordinary leas squares (DOLS) and he auoregressive disribued lag (ARDL) model developed by Pesaran e al. (2001). A number of panel daa sudies employed echniques like he Pedroni (2004) approach o es he hypohesis of no coinegraion beween ourism receips and economic growh. Hardly any of he sudies focussing on ourism and economic growh move beyond he framework of parameer consancy by using rolling window or ime-varying coefficiens esimaion echniques. In a survey of empirical research on he maer, Brida and Pulina (2010) repor he findings of 38 sudies published beween 2002 and 2010. The TLGH is validaed for he majoriy of he counries sudied, e.g. Tunisia (Belloumi, 2010), Souh Africa (Akinboade and Braimoh, 2010), Mauriius (Durbarry, 2004), Anigua and Bermuda (Schuber e al., 2010), Chile (Brida and Risso, 2009), Colombia (Brida e al., 2009), Uruguay (Brida e al., 2008a), Mexico (Brida e al., 2008b), Nicaragua (Croes and Vanegas, 2008), Turkey (Gunduz and Haemi-J, 2005), Greece (Drisakis, 2004). A bidirecional Granger causaliy is found in Malaysia (Lean and Tang, 2009), Taiwan (Kim e al., 2006), Spain (Corés and Pulina, 2010; Nowak e al., 2007), Mala (Kaircioglu, 2009b), Turkey (Ongan and Demiroz, 2005), whereas a unidirecional emporal relaionship running from economic developmen o ourism aciviy is deeced for Korea (Oh, 2005), Fiji, Tonga, Solomon Islands and Papua Guiniea (Narayan e al., 2010), and Cyprus (Kaircioglu, 2009a). In he African conex, hree sudies ha apply he VECM mehodology o es for Granger causaliy include Belloumi (2010), Akinboade and Braimoh (2010) and Durbarry (2004) wih focus on Tunisia, Souh Africa and Mauriius, respecively. Belloumi (2010) includes ourism receips, GDP and real effecive exchange rae in a VECM for Tunisia, and finds ha ourism receips Granger causes GDP in he long run. Akinboade and Braimoh (2010) include ourism receips, GDP, real effecive exchange rae and expors in a VECM for Souh Africa and esablish boh shor and long-run Granger causaliy running from ourism receips o GDP. In a sudy on Mauriius, Durberry (2004) includes ourism receips, GDP, physical and human capial and expors in a VECM. The findings include he exisence of a long-run bidirecional relaionship beween expors and GDP, while expors Granger causes GDP in he shor run. Lee and Chang (2008) in heir panel daa analysis concluded ha here is a unidirecional causaliy relaionship from ourism o economic growh in OECD counries bu a bidirecional relaionship in non- OECD counries. Brau e al. (2003) suggesed ha he smaller he counry, he higher he economic growh when he ourism is considered a key facor for he economy. In agreemen o his, Eugenio-Marin e al. (2004) found evidence ha such a relaionship exiss bu i is sronger for less developed economies bu no 3
for developed counries and Oh (2005) shows ha he relaionship is weaker for counries ha are no dependen on ourism. Even wihin sudies for he same counry, he resuls vary when choosing differen ime periods or mehodology. For example, for Turkey, Gunduz and Haemi-J (2005) find a unidirecional causaliy from ourism o economic growh using leveraged boosrap causaliy ess for he period 1963-2002; Ongan and Demiroz (2005) sugges a bidirecional causaliy relaionship for he period 1980Q1-2004Q2 using he Johansen echnique and vecor error correcion modelling; while Kaircioglu (2009c) employing he bounds es and Johansen approach concluded no coinegraing relaionship for he period 1960 o 2006. Arslanurk e al. (2011) aribue hese conflicing resuls o he fac ha he exisence and direcion of he relaionship beween ourism receips and economic growh may change hrough he years, a fac ha sandard Granger causaliy ess ha are commonly used canno pick up. They suppor he argumen of Hall e al. (2009, 2010) ha ime-varying esimaions can be he soluion o he problems of unknown funcional forms, specificaion errors and spurious conclusions, and sress he need for examining he ourismeconomic growh phenomenon for individual counries separaely and in a ime-varying conex. When applying rolling window and ime-varying parameer esimaion echniques for he period 1963 o 2006, hey show ha GDP has no predicive power for ourism receips in Turkey; however ourism receips have a posiive-predicive conen for GDP, bu only following he early 1980s, a resul ha is supporive of he findings of Gunduz and Haemi-J (2005). In order o analyse he ime-varying linkage beween real ourism receips and real GDP for Souh Africa for he period 1960 o 2011, we use a ime-varying VECM framework o conrol for srucural changes and regime shifs. We suppor he argumen of Arslanurk e al. (2011) ha sandard Granger causaliy ess overlook he probable non-consancy of causal relaionships and argue he poenial nonconsancy of he relaionship beween ourism and economic performance in he case of Souh Africa based on he numerous srucural changes wihin he Souh African economy over he sample period. Moreover, parially due o Souh Africa s isolaion in erms of inernaional rade and ourism beween he mid-1980s and early 1990s, limied aenion was given o his specific secor. Since 1994 however, he counry has improved is ourism posiion (Saayman and Saayman, 2008) and even more recenly, afer a number of evens ha araced ouriss from all over he world such as he Soccer World Cup in 2010. The main conribuion of his paper is ha i is he firs of is kind o examine he Souh African ourism secor and is relaionship wih economic growh in a ime varying esimaion framework. A furher innovaion on he Arslanurk e al. (2011) paper is ha we invesigae a poenial causal relaionship wihin he VECM conex by esing for shor-run, long-run and srong causaliy in a sae-space framework. The ess inroduces in his sudy are rue ime-varying Granger causaliy ess where all parameers are fully allowed o change wih ime. The res of he paper is organised as follows: Secion 2 describes he mehodology used, while Secion 3 discusses he daa used and empirical resuls of he analysis. The las secion concludes. 4
2. Mehodology Granger non-causaliy ess are used o deermine wheher one series significanly forecass anoher (Granger, 1969). The null hypohesis generally saes he non-exisence of one causal relaionship for he whole sample. However, a ime-varying causal relaionship may exis in which case causaliy migh no be applicable in he whole sample; ha is o say, a variable migh no Granger-cause anoher variable in some periods and migh Granger cause ha variable in oher periods. When he causal relaionship beween wo variables is no sable and he non-causaliy is no rejeced in a consan parameer model esimaed for he full sample, wha has been rejeced is no clear (Arslanurk e al., 2011). When here are srucural changes and policy shifs presen, ime series migh be said no o have a sable single regime. Agains his backdrop our vanage poin is o es for he exisence of a long-run coinegraing relaionship beween real ourism receips and real GDP. If such a relaionship is found o exis, a full sample Granger causaliy es can be carried ou wihin a VECM framework. Consider a bivariae VECM(p) beween ourism receips (TR ) and GDP (G ): é ë DG DTR ù é û = j 10 ë j 20 ù é û + g 1 ë g 2 ù p é û ECT + j 11-1 å j=1 ë j 21 ( j) ( j) ( j) j 12 ( j) j 22 ùé ûë DG - j DTR - j where ECT -1 is a lagged erm derived from he long-run coinegraing relaionship beween ourism receips and GDP series, 1 and 2 ù é + û ë are uncorrelaed disurbance erms wih zero mean and finie variance. We esimae he VECM in (1) for he full sample and es he null hypohesis ha ourism receips does no e 1 e 2 ù û (1) Granger cause GDP and ha GDP does no Granger cause ourism receips These Granger causaliy ess, however, assume parameer consancy in he model over ime, an assumpion, as saed, which may no hold. Salman and Shukur (2004) showed ha when he assumpion of parameer consancy is violaed, Granger causaliy ess can provide misleading inference abou he underlying causal relaionship. A model s srucure may deviae from assumed sabiliy in numerous ways, leading o many poenial alernaive specificaions agains he null hypohesis of sabiliy. Therefore hose ess ha leave he form of insabiliy unspecified possess desirable properies. Considering he many alernaives for pracical problems, researchers require (1) a wide variey of ess o ensure ha hese ess exhibi power agains a conceivable number of alernaives, as some ess indeed possess lile power agains violaions of heir assumpions, such as saionariy, no auocorrelaion, no ouliers, ec., and (2) ools ha permi he undersanding of he naure of deviaions from sabiliy so ha he researcher can dae he srucural change along wih he causes. In view of his, in his sudy, we include a baery of ess ha possess power agains boh specific alernaives and unspecified alernaives, and use rolling and recursive esimaion and ess ha permis he deerminaion of he form of deviaions from sabiliy and also o dae he srucural changes. 5
Firsly, we consider F ess proposed by Andrews (1993) and Andrews and Ploberger (1994) ha assume a single srucural change under he alernaive a an unknown ime period. They propose hree ypes of F ess: Sup-F, Ave-F and Exp-F eiher based on Wald, LM, or LR saisics. We implemen LR based F ess o es for shor-run sabiliy in he VAR model. Secondly, we employ a es o es long-run sabiliy, namely he Nyblom (1989) LM es based on he ML scores, denoed L c. Finally, we urn o recursive and rolling flucuaion ess, specifically he modified ME ess proposed by Kuan and Chen (1994) o es long-run parameer sabiliy in he VAR model. We apply he same ess o he long-run relaionship beween ourism receips and GDP, esimaed by FM-OLS. For a deailed discussion of he differen ess, refer o Balcilar e al. (2013). In conducing rolling esimaion, he VECM in (1) is esimaed for a ime span of 15 years rolling hrough = τ 14, τ 13,...,τ, τ = 15,...,T. For he recursive esimaion, 15 observaions are used, and hen one period a a ime is added o he end of he sample recursively. In order o esimae he order of he VECM in (1), we fi a VECM model o he whole sample period and deermine he opimal order using he Akaike informaion crierion (AIC). For all rolling and recursive VECM esimaes, he AIC is also used o deermine he lag lengh. Given he oucome of various sabiliy ess, we invesigae he issue of non-consancy of parameers furher. Alhough he rolling VECM esimaes may indicae a ime-varying relaionship beween he ourism receip and GDP series, he parameer esimaes may no be reliable due o he small sample size. Rolling esimaion is also no an opimal mehod o esimae ime-varying parameers. In order o esimae he ime variaion opimally we use a VECM wih ime-varying coefficiens. This approach allows us o overcome he shorcomings of rolling window esimaion. Insead of spliing he sample ino several subsamples, he ime-varying coefficiens capure he change in he dynamic relaionship and enables exac daing of he ransiion. In is mos general form, he ime-varying VECM(p) model for ourism receips and GDP series can be wrien as follows: é ë DG DTR ù é û = j 10, ë j 20, ù é + g 1, û ë g 2, ù p é ( j) ECT + j 11, -1 å ( j) û j=1 ë j 21, ( j) j 12, ( j) j 22, ùé ûë DG - j DTR - j where he error correcion coefficiens 1, and 2, are also ime-varying. In he empirical secion, using coinegraion ess, we show ha a long-run relaionship beween he ourism receips and GDP series exiss. Therefore, he coinegraion parameers 0 and 1 in ù é + û ë ECT G 0 1 invarian. In order o wrie Equaion (2) compacly, we define he following marices: é DG y = ë DTR ù é, j = j ( j) 11, j, ( j) û j ë 21, ( j) j 12, ( j) j 22, Then, Equaion (2) can be wrien as ù é, g = g 1, g û ë 2, ù é, j = j 10, 0, j û ë 20, e 1 e 2 ù û (2) TR are modelled as ime ù é, e = e 1 e û ë 2 ù û y = A Z -1 + e (3) 6
where Z is he vecor of variables defined as Z = (1,ECT,Dy,...,Dy -p+1 ) and A is he marix of parameers defined as A = ( j 0,, a, j 1,,..., j p, ). Here, e is a 2 1 vecor of whie noise wih var(e ) = R. We assume ha each elemen of A varies according o a random walk. The random walk specificaion is flexible, in ha i places a minimal resricion on he srucure of he ime variaion. Esimaion of he model parameers in Equaion (3) can be accomplished by Gaussian quasi-maximum likelihood (Durbin and Koopman, 2001) evaluaed using a Kalman filer (e.g. Anderson and Moore, 1979). In order o apply he Kalman filer, he model is represened in he sae-space form (see, e.g., Shumway and Soffer, 2000). Vecorizing boh sides of Equaion (3), he sae-space represenaion can be wrien as follows: where y = ( Z -1 Ä I 2 )a + e a = Ba -1 + v is he k-dimensional sae vecor defined as a = vec[j 0,,g,j 0,,...,j p, ] wih I 2 denoing a 2 2 ideniy marix, B denoing a k k ideniy marix, and v is a whie noise wih var(v ) = Q. We (4) assume ha he sae noise v and he observaion noise are conemporaneously correlaed wih cov(e,v ) = S, and zero oherwise. Le he bes linear predicor of 1 given he daa {y 1,..., y } as 1, and denoe covariance marix of he predicion error 1 1 as P 1. The Kalman filer can be used o obain he predicors and heir covariance marices successively as new observaions become available. The innovaion sequence {y 1,..., y } and given by u is defined as he bes linear predicion of y given he daa 1 ( 1 2) (5) u y Z I wih he innovaion covariance marix obained as -1 S =a P a + R (6) Since we will be using a boosrap mehod, i is more convenien o work wih sandardized innovaions e u (7) 12 which guaranees ha hese innovaions have, a leas, he same firs wo momens. The sae-pace model in Equaion (4) can equivalenly be wrien as x = F x -1 + H e (8) where é x = ë a +1 y ù é, F = û ë I k ( Z -1 Ä I 2 ) ù é, H = û ë [P -1-1 2 ( Z -1 Ä I 2 ) + S]S S 1 2 ù. û 7
Several sudies found evidence ha sample size mus be fairly large before asympoic resuls are applicable for he ime-varying parameer (TVP) model we consider (Den and Min, 1978; Ansley and Newbold, 1980). In his paper we use he boosrap algorihm of Soffer and Wall (1991) for sae-space models. The boosrap mehod is preferred for wo reasons. Firs, our sample size is relaively small and boosrap can approximae he empirical disribuion of parameers fairly well (Efron, 1979). Second, i allows us o consruc confidence inervals for he esimaes of oal impac of ourism receips on GDP ( * y TR, * i, p å ( j) = ĵ 21, j=1, i TR, G ) and GDP on ourism receips (y * G, p å ( j) = ĵ 12, j=1 ). Afer obaining B boosrapped esimaes of, he values are ordered and he100 h value is aken as he lower limi and 100(1 )h value is aken as he upper limi o consruc a (1 2 )% confidence inerval. For a deailed exposiion of seps in he boosrap procedure, refer o Arslanurk e al. (2011). 3. Daa and Empirical Resuls In his secion we apply he procedure described above o invesigae he ime-varying linkages beween ourism receips and GDP using annual daa from 1960 o 2011 for Souh Africa. The daa were obained from he daabases of he Souh African Reserve Bank (SARB) and Global Financial Daabase (GFD). To invesigae hese linkages, we use he real ourism receips and real GDP series. In order o ge he real series ha we consider, he ourism receips and GDP series are divided by he consumer price index and GDP deflaor, respecively. For purposes of he empirical analysis, boh series are expressed in logarihmic form. Figure 1 illusraes he rend of ourism receips and GDP for Souh Africa for he period 1960 o 2011. I can be noed ha boh variables experienced an upward rend hrough he examined period. A slowdown is eviden, in ourism receips in paricular, from he laer par of he 1970s hrough he 1980s and unil he early 1990s. This period is characerised by poliical unres, economic sancions, he deb sandsill agreemen and inernaional isolaion. These include evens like he 1976 Soweo uprising, and he deah of poliical acivis Seve Biko in Sepember of 1977. By 1980 he world has decisively urned agains Souh Africa. The declaraion of a sae of emergency in 1985 was followed by he deb sandsill agreemen and poliical, culural and economic sancions. Aparheid was however dismanled in a series of negaions from 1990 o 1993, saring wih he release of Nelson Mandela in February 1990, and finally culminaing in he esablishmen of a democracy in 1994. A paricularly seep increase from 1992 onwards is noable in he ourism receips series while real GDP also grew a more buoyan raes pos 1994. Boh series show a downurn coinciding and following he recen global financial crisis of 2008/09. Figure 1. Annual Real GDP and Real Tourism Receips of Souh Africa for 1960-2011 8
We repor he Augmened Dickey-Fuller uni roo es of Said and Dickey (1984) and he MZ uni roo es of Ng and Perron (2001) o deermine he order of inegraion of he real ourism receips and real GDP series. Table 1 repors he resuls of esing he null of non-saionariy (uni roo) agains he alernaive of saionariy (no uni roo). According o he resuls in Table 1, boh he ADF and MZ ess fail o rejec he null hypohesis of non-saionariy for ourism receips and he GDP. The es resuls furher indicae ha he firs differences of he series do rejec he null of a uni roo. Therefore we conclude ha boh real ourism receips and real GDP conform o I(1) processes. Table 1. Uni roo es resuls for Tourism receips and GDP of Souh Africa Level a b c d a Series ADF µ ADF MZ MZ ADF µ Firs Differences b ADF c MZ d MZ Tourism receips -0.92 (1) -1.82 (1) 1.46-5.66-3.75 *** (1) -3.73 ** (1) -22.07 *** -23.38 ** GDP -0.83 (1) -2.39 (1) 1.84-1.47-4.36 * (1) -4.27 *** (1) -18.77 *** -20.17 ** Noes: *,**,*** indicae significance a he 10, 5 and 1 per cen levels, respecively. a Tes allows for a consan; one-sided es of he null hypohesis ha he variable has a uni roo; 10, 5, 1 per cen significance criical value equals -2.58, -2.89, -3.51, respecively. b Tes allows for a consan and a linear rend; one-sided es of he null hypohesis ha he variable has a uni roo; 10, 5, 1 per cen criical value equals -3.15, -3.45, -4.04, respecively. c Tes allows for a consan; one-sided es of he null hypohesis ha he variable has a uni roo; 10, 5, 1 per cen criical values equals -5.7, - 8.1, -13.8, respecively. d Tes allows for a consan and a linear rend; one-sided es of he null hypohesis ha he variable has a uni roo; 10, 5, 1 per cen criical values equals -14.2, -17.3, -23.8, respecively. 9
Because an error correcion erm is an essenial par of Granger causaliy analysis, nex we es for a common sochasic rend, which implies he exisence of a coinegraing relaionship beween GDP and ourism receips. According o Granger (1988), if he causaliy ess do no incorporae an error correcion erm, hey may lead o incorrec conclusions. Since boh series conain a sochasic rend, a common sochasic rend is likely o occur; ha is he series may be coinegraed. We use Johansen s (1991) maximum likelihood mehod o examine wheher or no he ourism receip and GDP series are coinegraed. Table 2 repors he Johansen coinegraion race es saisic. The es resuls indicae ha he null hypohesis of no coinegraion beween he ourism receips and GDP series is rejeced a he 5 per cen significance level. Table 2. Coinegraion es resuls for ourism receips and GDP of Souh Africa Variables Null Hypohesis a and b Trace Tes r = 0 28.43 Tourism receips and GDP r 1 3.08 Noe: and * indicae significance a he 5 and 1 per cen levels, respecively. One-sided es of he null hypohesis ha he variables are no coinegraed; repored criical values are he Oserwald-Lenum (1992) criical values; 5 and 1 per cen criical values equal 9.24 and 12.97 for r 1, respecively; and 19.96 and 24.60 for r = 0, respecively. Considering he coinegraion beween he ourism receips and GDP series, ha is ha ourism receips and GDP mainain a long-run relaionship in levels, here should be causaliy a leas in one direcion (Engle and Granger, 1987). In order o examine he ype of causaliy beween he series, we firs perform a full sample Granger-causaliy es. We use he Akaike informaion crierion (AIC) for deermining he opimal number of lags in he VAR model. Saring wih p=1, we sequenially increase he lag of VAR model up o en, and selec he lag order wih minimum AIC. As suggesed by AIC, he opimal lag order is 1. 1 Nex a VECM as specified in (1) is used for he full sample o es he null hypoheses ha ourism receips does no Granger cause GDP (j (1) 12 = 0) and ha GDP does no Granger cause ourism receips 2 (j (1) 21 = 0). Table 3. Pair-wise Granger-causaliy es resuls beween ourism receips and GDP series in a VECM model H o : Tourism receip does no Granger cause GDP H o : GDP does no Granger cause ourism receip Lags F-Saisic p-value F-Saisic p-value 1 2.024 0.143 1.745 0.186 Noes: Granger causaliy ess are for he join non-causaliy boh in he shor- and long run * denoes significance a he 5 per cen significance level. 1 The lag order p we refer o relaes o he VECM model and he order of he level VAR is p+1. 2 These resricions es for he shor-run causaliy. The long-run causaliy can be esed by zero resricions on he coefficiens relaing o he error-correcion erm. 10
The resuls show ha we fail o rejec he null hypohesis ha ourism receip does no Granger cause GDP and vice-versa a any of he convenional significance levels. These findings show ha here is no shor-run Granger causaliy from ourism receips o GDP or from GDP o ourism receips when a full sample Granger-causaliy es is performed. We now urn o examining he sabiliy of he esimaed parameers in he long-run equaion as well as he VAR framework. Srucural changes may shif parameer values and he paern of he no causal relaionship may change over ime. Granger causaliy ess will show sensiiviy o sample period and order of he VAR model, if he parameers are emporally insable. The resuls of coinegraion and Granger causaliy ess based on he full sample also become invalid wih srucural breaks because hese ess assume parameer sabiliy. Full-sample parameer sabiliy Various ess exis o examine emporal sabiliy of VAR models (e.g. Hansen, 1992b; Andrews, 1993; Andrews and Ploberger, 1994). Alhough we can apply hese ess in a sraighforward way for saionary models, he variables in our model are non-saionary and coinegraed. We need o accommodae he possibiliy of his inegraion (coinegraion) propery because in a coinegraed VAR, he variables form a vecor error correcion model (VECM). Thus we need o invesigae he sabiliy of boh he long-run coinegraion and he shor-run dynamic adjusmen parameers. If he long-run or coinegraion parameers prove o be sable, hen he model exhibis long-run sabiliy. Addiionally, if he shor-run parameers are also sable, hen he model exhibis full srucural sabiliy. Since he esimaors of coinegraion parameers are superconsisen we can spli he parameer sabiliy esing procedure ino wo seps. Firs, we es he sabiliy of he coinegraion parameers. Second, if long-run parameers prove sable, hen we can es he sabiliy of he shor-run parameers. To examine he sabiliy of coinegraion parameers, we use he L c es of Nyblom (1989) and Hansen (1992a). This Nyblom-Hansen saisic ess for parameer consancy agains he alernaive hypohesis ha he parameers follow a random walk process and, herefore are ime-varying, since he firs wo momens of a random walk are ime dependen. Nex we use he Sup-F, Mean-F, and Exp-F ess developed by Andrews (1993) and Andrews and Ploberger (1994) o invesigae he sabiliy of he shor-run parameers. We compue hese ess from he sequence of LR saisics ha ess consan parameers agains he alernaive of a one-ime srucural change a each possible poin of ime in he full sample. These ess exhibi asympoic non-sandard disribuions and Andrews (1993) and Andrews and Ploberger (1994) repor he criical values. To avoid he use of asympoic disribuions, however, we calculae he criical values using he parameric boosrap procedure. The oucome of hese ess for parameer consancy o derive inference on he emporal sabiliy of he coefficiens of he VAR model for ourism receips and GDP is presened in Table 4. Boosrap p-values come from a boosrap approximaion o he null disribuion of he es saisics, consruced by means of Mone-Carlo simulaion using 2,000 samples generaed from a VAR model wih consan parameers. We 11
calculae he L c es for each equaion separaely using he FM-OLS esimaor of Phillips and Hansen (1990). The Sup-F, Mean-F and Exp-F ess require rimming a he ends of he sample. Following Andrews (1993), we rim 15 per cen from boh ends and calculae hese ess for he fracion of he sample in [0.15, 0.85]. The resuls for he L c es of sabiliy of he coinegraion parameers indicae ha boh he ourism receips and GDP equaions exhibi sable long-run parameers a he 5 per cen significance level. Tha is, we find evidence of coinegraion. There is also evidence of parameer consancy, or sabiliy, in he unresriced VAR(2) sysem. Nex, we consider he shor-run parameer sabiliy, using he Sup-F, Mean-F and Exp-F es resul, also repored in Table 4. The Sup-F saisics es parameer consancy agains a one-ime sharp shif in parameers. On he oher hand he Mean-F and Exp-F, which assumes ha parameers follow a maringale process, es for gradual shifing in he regime. Boh he Mean-F and Exp-F saisics es he overall consancy of he parameers (i.e. hey invesigae wheher he underlying relaionship among he variables says sable over ime). In addiion, Andrews and Ploberger (1994) show ha he Mean-F and Exp-F are boh opimal ess. The resuls repored for he sequenial Sup-F, Mean-F and Exp-F sugges ha significan evidence of parameer non-consancy exiss in he ourism receips and GDP equaions, as well as in he enire VAR sysem a he 5 per cen level, excep for he Mean-F es for he GDP equaion. In sum, he evidence obained from he parameer sabiliy ess indicae ha he coinegraed VAR model does exhibi consan long-run parameers, whereas he shor-run dynamics of he model show parameer insabiliy. The L c, Sup-F, Mean-F and Exp-F ess prove consisen in his regard. Table 4. Parameer Sabiliy Tess in VAR(2) Model Tourism Receips Equaion Real GDP Equaion VAR(2) Sysem Saisics Boosrap p-value Saisics Boosrap p-value Saisics Boosrap p-value Mean-F 23.11 <0.01 6.00 0.26 10.73 0.04 Exp-F 53.48 <0.01 8.00 <0.01 8.12 0.02 Sup-F 114.19 <0.01 22.77 <0.01 22.74 0.02 L c 0.33 0.75 0.44 0.67 0.69 0.57 Rolling L 2 norm 0.90 0.33 1.16 0.15 2.67 0.01 Recursive L 2 norm 1.93 0.03 0.94 0.31 2.50 <0.01 Noes: We calculae p-values using 2,000 boosrap repeiions. To allow for a se of alernaive ess, we also esimae he coinegraion equaion beween ourism receips and real GDP as follows: 12
GDP = + *TR + (9) where GDP denoes real GDP and TR denoes ourism receips. We esimae he parameers in equaion (9) using he FM-OLS esimaor. Table 5 repors he resuls of he various parameer sabiliy ess. The Nyblom-Hansen L c es canno rejec he null of consan parameers a any reasonable level. Similarly, he Mean-F and Exp-F ess canno rejec he null hypohesis of unchanging parameers in he coinegraion equaion. In oher words, we do no find evidence of gradual shifing of he parameers of he coinegraion equaion. The Sup-F es, however, suggess a one-ime shif in he coinegraion relaionship. Table 5. Parameer Sabiliy Tess in Long-Run Relaionship FM-OLS Mean-F Exp-F Sup-F L c Rolling L 2 Recursive norm L 2 norm Equaion: GDP = + *TR 42.22 35.92 78.51 0.27 3.80 2.72 Boosrap p value 0.92 0.77 <0.01 0.53 <0.01 <0.01 Noes: We calculae p-value using 2,000 boosrap repeiions. Recursive and Rolling Window Parameer Sabiliy Since he parameer consancy ess poin o srucural change, we esimae he VAR model using recursive and rolling window regression echniques. The recursive esimaor sars wih a benchmark sample period and hen adds one observaion a a ime keeping all observaions in prior samples so ha he sample size grows by one wih each ieraion. The rolling-window esimaor, also known as fixed-window esimaor, alers he fixed lengh benchmark sample by moving sequenially from he beginning o he end of sample by adding one observaion from he forward direcion and dropping one from he end. Assume ha each rolling subsample includes 15 annual observaions (i.e., he window size is equal o 15). In each sep for he recursive and moving window models, we deermine a VAR model using he AIC o choose he lag lengh and perform he Granger causaliy ess using he residual based (RB) boosrap mehod on each subsample. This provides us wih a sequence of 36 causaliy ess insead of jus one. The recursive and rolling esimaions ha we adop are jusified for a number of reasons. Firs, recursive and rolling esimaions allow he relaionship beween he variables o evolve hrough ime. Second, he presence of srucural changes inroduces insabiliy across differen subsamples and recursive and rolling esimaions convenienly capure his; in our case, by considering a sequence of 36 differen subsamples (saring wih he benchmark sample from 1962 o 1976). The rolling window uses a 15-year fixed window. For he rolling esimaions, he window size is an imporan choice parameer. Indeed, he window size conrols he number of observaions covered in each subsample and deermines he number of rolling esimaes, since a larger window size reduces he number of observaions available for esimaion. More imporanly, he window size conrols he precision and represenaiveness of he subsample esimaes. A large window size increases he precision of esimaes, bu may reduce he represenaiveness, paricularly, in he presence of heerogeneiy. On he conrary, a small window size will reduce heerogeneiy and 13
increase represenaiveness of parameers, bu i may increase he sandard error of esimaes, which reduces accuracy. Therefore, he choice of he window size should achieve a balance of no oo large or oo small, hus, balancing he rade-off beween accuracy and represenaiveness. We follow Kouris e al. (2008) and Balcilar e al. (2013) and use a rolling window of small size (i.e., 15 annual observaions) o guard agains heerogeneiy. Our choice of small window size may lead o imprecise esimaes. Therefore, we apply he boosrap echnique o each subsample esimaion in order o obain more precise parameer esimaes and ess. No sric crierion exiss for selecing he window size in rolling window esimaion. Pesaran and Timmerman (2005) examine he window size under srucural change in erms of roo mean square error. They show ha opimal window size depends on persisence and size of he break. Their Mone Carlo simulaions shows ha we can minimize he bias in auoregressive (AR) parameers wih window sizes as low as 20 when frequen breaks exis. In deermining he window size, we need o balance beween wo conflicing demands. Firs, he accuracy of parameer esimaes depends on he degree of freedom and requires a larger window size for higher accuracy. Second, he presence of muliple regime shifs increases he probabiliy of including some of hese muliple shifs in he windowed sample. In order o reduce he risk of including muliple shifs in he subsamples, he window size should be small. Based on he simulaion resuls in Pesaran and Timmerman (2005) we use a window size of 15 (his excludes he observaions required for lags and hence is he acual number of observaions in he VAR). Firs consider he VAR(2) sysem. The recursive ME-L 2 saisic is repored in he las row in Table 4. Only he GDP equaion shows evidence of parameer sabiliy, while he ourism receip equaion as well as he VAR(2) sysem show evidence of parameer insabiliy, a he 5 per cen and 1 per cen levels of significance, respecively. Figure 2(a) provides a sample-by-sample picure of he ME-L 2 es saisic for he individual equaions as well as for he VAR sysem. 3. The figure indicaes several periods of ime when we can rejec he null of parameer sabiliy a he 5 per cen level, namely 1975 o 1976 (coinciding wih poliical evens such as he Soweo uprising and he deah of poliical acivis, Seve Biko), a relaive long period from 1983 o 1998 (which includes he deb sandsill agreemen and a period of sancions of a poliical, economic and culural naure) and 2001 (a period marked by volailiy in he Souh African currency marke). Insabiliy in hese selecive periods also reflecs proximaely insabiliy in he GDP equaion, even hough he ME-L 2 es indicaes parameer sabiliy overall. Figure 2. Sabiliy Tess for VAR(2) Model 3 Figure 2 only repors he significance level and mean L 2 norm es for he VAR sysem and no for he individual equaions. The mean value is indicaed wih a doed line, while he 5 per cen significance level is indicaed a solid horizonal line. 14
Noes: Rolling window size is 15 observaions, approximaely 30% of he observaions. Rolling esimaes are cenered a [h/2] where h is he window size and [a] is he ineger par of a. F ess use 15 per cen rimming from boh ends. The rolling ME-L 2 es saisic in he second las row in Table 4 implies ha boh he ourism receip and GDP equaions exhibi parameer sabiliy, while he ME-L 2 es for he VAR sysem implies parameer insabiliy a he 1 per cen level. Figure 2(b) indicaes ha we rejec he null hypohesis of sable parameers for a number of shorer periods, namely 1976 and 1979 and from 1980 o 1987, 1990, 1993 o 1998 and again in 2001. Insabiliy in all periods reflecs insabiliy in he ourism receip equaion.finally, consider he long-run coinegraing relaionship. Table 5 repors he Sup-F, Mean-F, Exp-F and L c es resuls. All ess, wih he excepion of he Sup-F es, suppor parameer sabiliy in he long run. For boh he rolling and recursive specificaions however, we find ha he parameers of he long-run rend equaion exhibi parameer insabiliy a he 1 per cen level. For he recursive regressions, Figure 3(a) plos he sample by sample ME-L 2 es saisic for he long-run equaion. The ME-L 2 es saisic indicaes ha he parameers do no remain sable over he enire period. In addiion, he saisics repored sugges ha parameer insabiliy begins shorly afer he beginning of he full sample and ends jus before he end, ha is, he es suggess insabiliy from 1962 o 15
2005. For he rolling regressions, Figure 3(b) plos he ME-L 2 saisics. The saisics repored for each subsample over he enire period, suggess parameer insabiliy beween 1984 and 2003. Figure 3. Sabiliy Tess for he Long-Run Relaionship based on FM-OLS Esimaion We nex proceed o carry ou Granger non-causaliy ess in a ime-varying parameer VECM conex. We es 7 differen hypoheses as se ou in Table 6, namely ha of consan parameers in he VECM, shor-run causaliy, long-run causaliy, and srong causaliy, which implies boh shor and long-run causaliy. The associaed resricions in he VECM and sae space forms are also indicaed for all 7 hypoheses. In able 7, informaion crieria and log likelihood values for he unresriced, full sample model is compared o a number of differen resriced models. 16
Table 6. Tesable Full-Sample Non-causaliy Hypoheses and Resricions in TVP-VECM Model Hypohesis Resricions in VECM form Resricions in SS form (k H 01 : Consan parameers g i, = g i, f ) (k ij, = f ) ij, i, j = 1,2 Q = 0 Shor-run causaliy ( k ) H 02 : T. receips =¹Þ GDP f 12, ( k ) H 03 : GDP =¹ÞT. receips f 21, = 0 q j1 j 1 = 0, b j1 j 1 = 0 = 0 q j2 j 2 = 0, b j2 j 2 = 0 Long-run causaliy H 04 : T. receips =¹Þ GDP g 1, = 0 q j3 j 3 = 0, b j3 j 3 = 0 H 05 : GDP =¹ÞT. receips g 2, = 0 q j4 j 4 = 0, b j4 j 4 = 0 Srong causaliy ( k ) H 06 : T. receips =¹Þ GDP g 1, = 0, f 12, = 0 q j1 = q j 1 j3 = 0, b j 3 j1 = b j 1 j3 = 0 j 3 ( k ) H 07 : GDP =¹ÞT. receips g 2, = 0, f 21, = 0 q j2 = q j 2 j4 = 0, b j 4 j2 = b j 2 j4 = 0 j 4 Noe: X =¹ÞY means variable X does no Granger cause variable Y. q ij is he elemen in ih row and jh column of Q, similarly b ij is he elemen in ih row and jh column of B. Table 7. Informaion Crieria and Log Likelihood of Esimaed Sae Space Models Model under resricion: Resricion AIC AICc Log L Full model, Eq. 4 None -260.30-208.30 169.15 H 01 : Consan parameers g i, = g i, (k f (k ij, = f ) ij, i, j = 1,2-220.15-219.90 113.08 ( k ) H 02 : T. receips GDP f 12, = 0 =¹Þ ( k ) H 03 : GDP T. receips f 21, = 0 H 04 : T. receips GDP g 1, = 0 =¹Þ =¹Þ =¹Þ H 05 : GDP T. receip g 2, = 0 ( k ) H 06 : T. receips =¹Þ GDP g 1, = 0, f 12, = 0-186.95-157.77 124.47-177.40-148.22 119.70-178.55-149.37 120.27-181.42-152.25 121.71-212.21-196.21 130.11 ( k ) H 07 : GDP =¹ÞT. receips g 2, = 0, f 21, = 0-209.55-193.55 128.77 Noes: See Table 6 for definiion of resriced models. AIC is he Akaike informaion crierion, AICc is he small sample correced Akaike informaion crierion of Hurvich and Tsai (1989). logl is he value of he log likelihood. Table 8 repors full-sample boosrap Granger causaliy ess under he TVP-VECM specificaion in equaion (4). Firsly, he rejecion of he null of consan parameers is supporive of earlier sabiliy es resuls. We rejec he null ha he ourism receips variable does no Granger cause GDP in he shor run, and vice versa. We also rejec he null of long-run non-causaliy from ourism receips o GDP a he 1 per cen level, implying ha ourism receips have predicive power for GDP. From Figure 4(b) i is eviden ha his predicive conen is saisically significan from he 1990s onwards, a a significance level of 20 per cen. The speed of adjusmen coefficien of he GDP equaion, ranging beween -0.09 and -0.10, is 17
indicaive of a fairly slow speed of adjusmen afer deviaion from equilibrium due o an exernal shock (see Figure 4(a)). The coefficien is significan a he 5 per cen level for he enire period, indicaing ha ourism receips have long-run predicive conen for GDP over he sample period. Moreover, we also rejec he resricion g 2, = 0 a he 1 per cen level, his rejecion imply long-run causaliy from GDP o ourism receips. Therefore, we conclude ha boh ourism receips and GDP behave weakly endogenously wih respec o each oher and here is long-run bidirecional causaliy beween he wo variables.. Table 8. Full Sample Boosrap Granger Causaliy Tess Under TVP-VECM Hypohesis Resricions in SS form # resricion LR es Boosrap p-value H 01 : Consan parameers Q = 0 36 112.15 < 0.01 Shor-run causaliy H 02 : T. receips =¹Þ GDP q j1 j 1 = 0, b j1 j 1 = 0 2 89.36 < 0.01 H 03 : GDP =¹ÞT. receips q j2 j 2 = 0, b j2 j 2 = 0 2 98.91 < 0.01 Long-run causaliy H 04 : T. receips =¹Þ GDP q j3 j 3 = 0, b j3 j 3 = 0 2 97.76 < 0.01 H 05 : GDP =¹ÞT. receips q j4 j 4 = 0, b j4 j 4 = 0 2 96.88 < 0.01 Srong causaliy H 06 : T. receips =¹Þ GDP q j1 j 1 = q j3 j 3 = 0, b j1 j 1 = b j3 j 3 = 0 4 78.09 < 0.01 H 07 : GDP =¹ÞT. receips q j2 j 2 = q j4 j 4 = 0, b j2 j 2 = b j4 j 4 = 0 4 80.76 < 0.01 Noes: See Table 6 for definiion of resriced models. p-values are obained wih 2000 boosrap simulaions. Then finally, when esing he hypoheses of srong causaliy, we rejec he null ha ourism receips does no Granger cause GDP a he 1 per cen level, o conclude srong causaliy running from ourism receips o GDP. Tourism receips herefore have predicive conen for GDP boh in he shor and long run. Since boh GDP and ourism receips are no weakly exogenous, rejecion of he null of Granger non-causaliy hypoheses in Table 8 imply ha ourism receips and GDP have predicive power for each oher boh in he shor- and long-run. 18
Figure 4. Adjusmen Speed in GDP Equaion and Tes for he Long-Run Causaliy from Tourism Receips o GDP Noes: Parameers are esimaed by maximum likelihood of he sae-space model. The LR ess are calculaed sequenially for =1,2,,T using he mehod in Doran (1992). The p-values are obained using 2000 boosraps. 19
Figure 5. Adjusmen Speed in Tourism Receips Equaion and Tes for he Long-Run Causaliy from GDP o Tourism Receips Noes: See noes o Figure 4. Figures 6 displays he shor-run impacs of ourism receips on GDP and vice versa esimaed by he imevarying VECM. The TVP-VECM explicily models he parameer variaion and propagaion, using full sample informaion opimally, unlike rolling and recursive esimaion mehods ha are used as an alernaive o model srucural changes (Arslanurk e al., 2011). The sae-space model also enables exac daing of he shifs in he causal relaionship beween he series. In his sudy, he TVP model is esimaed wih 2,000 boosrap resample. The boosrap procedure yields 2,000 boosrapped esimaes of oal impac * * of ourism receips on GDP ( ) and GDP on ourism receips ( TR, * repor means of hese oal impac esimaes, ha is y TR, G, ) for each period. In Figure 6, we * * = å y TR, 2,000 and y G, * = å y G, 2,000. These are he boosrap esimaes of he ime-varying impacs beween he series. Figure 6 also repors he 20
* * 95% boosrap confidence inervals for he oal impac esimaes and TR, G,. We repor he mean and 95% confidence inerval esimaes of he impac of ourism receips on GDP in panel A of Figure 6. The * plo of indicaes ha he sign of he effec from ourism receip o GDP changed from posiive o TR, negaive in 1983 and back o posiive again in 1990. The negaive coefficien is however no significan, excep in 1985, coinciding wih he declaraion of a sae of emergency in he counry, he deb sandsill agreemen and he onse of economic sancions and inernaional isolaion. Figure 6. Esimaes of he Shor-Run Impacs Noes: Parameers are esimaed by maximum likelihood of he sae-space model. The confidence inervals are consruced using 2000 boosraps. The posiive impac afer 1990 remains posiive and significan for he res of he sample period. This posiive impac coincides wih he dismanling of Aparheid ha sared wih he release of Nelson Mandela in February 1990, evenually leading o he esablishmen of a democracy in 1994 and he full opening up 21
of he economy o inernaional ourism and rade. The boosrap esimaes of impac of GDP on ourism * receips, G,, are repored in panel B of Figure 6 for each period. The impac of GDP on ourism receips * is esimaed as approximaely zero for he whole sample period. The 95% confidence inervals for are marginally over zero and fairly consan, meaning ha GDP has predicive conen for ourism receips for he whole sample period we consider. In summary, he TVP model esimaes based on he Kalman filer show ha here is posiive impac from ourism receips o GDP for he enire sample period, wih he excepion of he period beween 1985 and 1990. G, Figure 7. Shor-run Granger Causaliy Tess Noes: See noes o Figure 4. We conclude by reporing p-values esing he hypoheses of shor-run and srong Granger causaliy wihin he ime-varying parameer framework. From Figure 7(a) we noe ha he shor-run causal relaionship 22
beween ourism receips and GDP only breaks down during he 1985-1990 period, he heigh of he Aparheid regime characerized by culural and economic sancions. From Figure 7(b) we marginally rejec he null of no Granger causaliy running from GDP o ourism receips for he enire period examined. When considering he ime-varying es for he hypoheses of no srong Granger causaliy, we see in Figure 8(a) ha we rejec he null of no Granger causaliy beween ourism receips and GDP a he 5 per cen level for he enire period examined. Once again he resricions g 2, = 0, f 21, ( k ) = 0are rejeced for he enire period, his implies he null of no srong Granger causaliy from GDP o ourism receips is rejeced. Figure 8. Srong Causaliy Tess Noes: See noes o Figure 4. 23
4. Conclusion This paper invesigaes he ime-varying causal links beween he real ourism receips and real GDP for Souh Africa by using sae-space ime-varying coefficiens and rolling and recursive window mehods for daa for covering 1960-2011 period. The mehodology employed in his paper allows us o overcome he economeric concerns ha could appear in he applicaion of Granger causaliy ess. Changes in economic policies worldwide as well poliical and social environmen could influence he causaliy paerns in he daa under consideraion. For his reason, we prefer o a use ime-varying parameer VECM. The empirical analyses yielded he following conclusions: Firsly, he resul obained from he full sample show ha here is no Granger causaliy beween ourism receips and GDP. On he oher hand, resuls obained from he sae-space ime-varying coefficiens and rolling window esimaion mehods o analyse he Granger causaliy based on VECM wih ime-varying parameers indicae ha he ourism receip posiively Granger-causes GDP wih a shor break in he period beween 1985 and 1990 and also GDP haa predicive power for ourism receips. In erms of he findings obained from he analyses, i could be held ha ourism receips have a posiive impac on he economic growh in Souh Africa. The resul from he saespace ime-varying coefficiens and rolling window mehods draw analogy wih he resuls of Akinboade and Barimoh (2010), Ongan and Demiroz (2005) and Arslanurk, Balcilar and Ozdemir (2011). References Akinboade, O. & Braimoh, L.A. (2010), Inernaional ourism and economic developmen in Souh Africa: A Granger causaliy es, Inernaional Journal of Tourism Research, 12, pp. 149-163. Anderson, B.D.O.; Moore, J.B. (1979). Opimal Filering, Englewood Cliffs: Prenice-Hall. Andrews, D.W.K. (1993). Tess for parameer insabiliy and srucural change wih unknown change poin. Economerica 61(4), 821-856. Andrews, D.W.K. and Ploberger, W. (1994) Opimal ess when a nuisance parameer is presen only under he alernaive, Economerica 62(6): 1383-1414. Ansley, C.F.; Newbold, P. (1980). Finie sample properies of esimaors for auoregressive moving average models, Journal of Economerics 13, 159 184. Arslanurk, Y., Balcilar, M. and Ozdemir, Z.A. (2011). Time-varying linkages beween ourism receips and economic growh in a small open economy, Economic Modelling 28: 664-671. Balaguer, J. & Canavella-Jordà, M. (2002), Tourism as a long-run economic growh facor: he Spanish case, Applied Economics, 34, pp. 877-884. Balassa, B. (1978), Expors and economic growh: Furher evidence, Journal of Developmen Economics, 5, pp. 181-189. Balassa B. (1988). The Lessons of Eas Asian Developmen: an Overview. Economic Developmen and Culural Change; 36; 273-290. 24