An Econometric Analysis of Aggregate Outbound Tourism Demand of Turkey

Transcription

1 MPRA Munich Personal RePEc Archive An Econometric Analysis of Aggregate Outboun Tourism Deman of Turkey Fera Halicioglu Department of Economics, Yeitepe University 008 Online at MPRA Paper No. 6765, poste 18. January 008 0:06 UTC

2 Presente at the 6 th DeHaan Tourism Management Conference: The Economics of Tourism, organize by the Christel DeHaan Tourism an Travel Research Institute, Nottingham University Business School Nottingham-UK, 18 th December 007 An Econometric Analysis of Aggregate Outboun Tourism Deman of Turkey Abstract This stuy attempts to examine empirically aggregate tourism outflows in the case of Turkey using the time series ata for the perio As far as this article is concerne, there exists no previous empirical work ealing with the tourist outflows from Turkey. The previous tourism stuies in the case of Turkey, by an large, focus on the inboun tourism eman analyses. As a eveloping country an an important tourism estination, Turkey has also been a significant source for generating a substantial number of tourists in recent years. Therefore, the tourist outflows from Turkey eserve to be analyse empirically too. The total tourist outflows from Turkey are relate to real income an relative prices. The bouns testing to cointegration proceure propose by Pesaran et al. (001) is employe to compute the short an long-run elasticities of income an relative prices. An augmente form of Granger causality analysis is conucte amongst the variables of outboun tourist flows, income an relative prices to etermine the irection of causality. In the long-run, causality runs interactively through the error correction term from income an relative prices to outboun tourist flows. However, in the short-run, causality runs only from income to outboun tourism flows. The aggregate tourism outflows equation is also checke for the parameter stability via the tests of cumulative sum (CUSUM) an cumulative sum of the squares (CUSUMSQ). The empirical results suggest that income is the most significant variable in explaining the total tourist outflows from Turkey an there exists a stable outboun tourism eman function. The results also provie important policy recommenations. Keywors: outboun tourism eman; cointegration; Granger causality; stability tests; Turkey. Corresponence to: Fera HALICIOGLU Department of Economics Yeitepe University Istanbul Turkey fhalicioglu@yeitepe.eu.tr fhalicioglu@yahoo.co.uk

3 Introuction Outboun tourism in eveloping countries is regare as a waste of valuable foreign reserves. Therefore, the authorities ten to restrict the outboun tourism eman via ifferent forms of obstacles an policies, such as excess exit taxes, restrictions on issuing passports, limiting the amount of foreign currency to take abroa an multiple rates for the ifferent purposes of foreign country visits. All these outboun tourism restrictions are aime at preserving the foreign reserves for essential imports an foreign ebt reuctions. Turkey also resorte to the outboun eman restriction policies until the 1980s because Turkey pursue an import substitution policy as a evelopment strategy until However, this evelopment policy faile in the late 1970s causing massive bottlenecks in inustries an foreign reserves eficits. In the last three ecaes, Turkey aopte the export-le growth evelopment policy with the implementation of several major economic reforms in the fiels of foreign trae, monetary an fiscal institutions, competition, state public enterprises, privatization, FDI, etc. The major aims of these economic reforms are to increase the efficiency of the economic resources an, hence, to close the economic evelopment gap between Turkey an the evelope countries. As of 005, Turkey ranke the 17 th biggest economy in the Worl with a population of over 73 million. Turkey s economic integration into the worl economy has been growing at a rapi rate since the early 1980s. To this en, the openness ratio, which is measure broaly as total imports an exports to gross omestic prouct, increase from 8.37% in 1970, to 15.51% in 1980 an to 46.4% in 005. Although, the average per capita income is far below that of the evelope countries, the real per capita rose to $ 3390 in 005, from $1896 in 1980 an from $1650 in Easing the travelling restrictions abroa an the relatively faster economic evelopment in Turkey have stimulate significantly the outboun tourist eman since the 1980s. As a consequence, the outboun tourism eman increase from 515,000 people with an expeniture of $1 million in 1970, to 1.65 million people with an expeniture of $104 million in 1980 an to 8.0 million people with an expeniture of $.87 billion in 005. The share of tourism expenitures in GDP appears to be very minimal, which is only 0.77% in 005. These tourism expeniture ratios are 0.06% an 0.14% for 1970 an 1980, respectively. Nevertheless, the average growth rate of outboun tourism eman in the perio of is 9.38% whilst the real GDP growth in the same perio is just 4.06%, inicating that there exists a substantially strong outboun tourism eman. It seems that the current upwar tren in the outboun tourism eman is likely to continue to grow faster, as Turkey becomes a full member of the European Union in the next ecae. Tourism eman moels have been use extensively to analyse the eman behaviour an eman management issues in aition to forecast the future levels of tourism eman. The empirical estimates of income an relative price elasticities have particular relevance for esigning appropriate income an pricing policies in the tourism sector. 1 These macroeconomic figures are obtaine from international financial statistics of IMF. The ratios are my own calculations from the same source. Outboun tourist numbers an tourism expeniture figures come from annual tourism statistics of TSI (Turkish Statistical Institute). The ratios an growth figures are my own calculations from the same source.

4 There is extensive literature examining the tourism eman functions in the context of eveloping an evelope countries using the single an multivariate cointegration techniques of the 1980s an 1990s. The results an implications of these stuies clearly epen on the unerlying variables, the econometric methos, ata frequency, an the evelopment stage of a country. Crouch (1994), Lim (1997), an Li et al. (005) provie very comprehensive surveys of empirical tourism eman stuies for the last four ecaes. These surveys reveal that most of the existing stuies ten to use the tourist arrivals/epartures an tourism revenues/expenitures as a epenent variable. The surveys also point out that the most wiely use explanatory variables are income an price/relative prices. The theoretical basis for the selection of these explanatory variables is relate to the consumer theory. However, many empirical stuies also use aitional explanatory variables ranging from transportation costs to time trens. The tourism eman equations are generally estimate in ouble logarithmic forms so that researchers obtain a irect estimate of elasticity of the epenent variable with respect to the explanatory variables. Recent econometric stuies appear to present both the short-run an long-run estimates of the explanatory variables. A large number of empirical papers on international tourism eman are foun in the literature an are ivie into two main categories. The first category consists of stuies that use moern time series an cointegration techniques in an attempt to moel an forecast the epenent variable between one or several pairs of countries. See, for example, Kulenran (1996), Wong (1997), Kim an Song (1998), Kulenran an Witt (001), Seighi an Theocharous (00), Song et al. (003) an Dritsakis (004), Charalambos (006), an Li et al. (006). The secon category inclues papers that estimate the eterminants of international tourism eman using classical multivariate regressions. For a etaile survey of this literature, see Crouch (1994), Witt an Witt (1995), an Lim (1997). The vast majority of international tourism eman stuies are base on the inboun of tourist flows rather than the outboun tourist flows. However, a few recent stuies on international tourism eman have presente empirical estimations of outboun tourism eman, see for example Song et al. (000) for UK; Lim (004) for Korea an Coshall (006) for UK. On researching the literature, one fins that there exist several empirical research stuies ealing with the inboun tourism eman for Turkey using both the traitional an moern econometric techniques; see for example, Uysal an Crompton (1984), Var et al. (1990), Ulengin (1995), Icoz et al. (1998), Akis (1998), Akal (004), an Halicioglu (004). However, no stuy has attempte to moel the outboun tourism eman for Turkey. Thus, this stuy seizes the opportunity to fill the gap in the literature. The motivation of this stuy is two fol: an increasing number of Turks are holiaying abroa which provies a goo rationale to ientify the eterminants of the outboun tourism eman of Turkey at an aggregate level an the bouns testing proceure to cointegration has not been use previously in the literature to estimate any outboun tourism eman. However, one shoul point out that the bouns testing proceure was recently employe to estimate the inboun tourism eman equations, 3

5 see for example Kumar (004) for Fiji; Halicioglu (004) for Turkey; an Mervar an Payne (007) for Croatia. The objectives of this stuy are as follows: i) to estimate the income an relative price elasticities of the outboun tourism eman both in the short-run an long-run using the ARDL approach to cointegration; ii) to establish the irection of causal relationships between outboun tourism eman, income an relative prices; an iii) to implement parameter stability tests of Brown et al. (1975) to ascertain stability or instability in the outboun tourism eman function. The remainer of this paper is organize as follows: the next section escribes the stuy s moel an methoology. The thir section iscusses the empirical results, an the last section conclues. Moel an econometric methoology Following the empirical literature in tourism economics, an aggregate outboun tourism eman regression moel for Turkey in ouble logarithmic form is constructe as: f t = a0 + a1 yt + a pt + ε t (1) where f t is aggregate tourist flows from Turkey, y t is real aggregate income, p t is exchange rate ajuste relative prices an ε t is the regression error term. As for the expecte signs in equation (1), one expects that a > 0 1 because higher real income shoul result in greater economic activity an stimulate outboun tourism eman. The aforementione empirical tourism eman surveys inicate that income elasticity estimates vary a great eal, but generally excee unity an below.0 confirming that international travel is a luxury item. The coefficient of the exchange rate ajuste relative price levels is expecte to be less than zero for the usual economic reasons, therefore, a < 0. The estimation results foun in the tourism eman surveys regaring prices are rather uneven since there seems to be no agreement about the appropriate range of this coefficient. Estimate price elasticities vary ramatically both within an across papers. For example, they are in the range of 0.05 to In the last two ecaes, several econometric proceures were employe to investigate the tourism eman functions. With regars to univariate cointegration approaches, there are several examples incluing Engle an Granger (1987) an the fully moifie OLS proceures of Phillips an Hansen (1990). There are also many examples of multivariate cointegration proceures of Johansen (1988), Johansen an Juselius (1990), an Johansen s (1996) full information maximum likelihoo technique. Song an Li (008) provies an excellent survey of the econometric proceures applie in the recent empirical stuies of tourism eman. A recent single cointegration approach, known as autoregressive-istribute lag (ARDL) of Pesaran et al. (001), has become popular amongst researchers. Pesaran et al. cointegration approach, also known as bouns testing, has certain econometric avantages in comparison to other single cointegration proceures. They are as follows: i) enogeneity problems an inability to test hypotheses on the estimate coefficients in the long-run associate with the Engle-Granger metho are avoie; ii) the long an short-run parameters of 4

6 the moel in question are estimate simultaneously; iii) the ARDL approach to testing for the existence of a long-run relationship between the variables in levels is applicable irrespective of whether the unerlying regressors are purely I(0), purely I(1), or fractionally integrate; iv) the small sample properties of the bouns testing approach are far superior to that of multivariate cointegration, as argue in Narayan (005). An ARDL representation of equation (1) is formulate as follows: t m m m 0 + a1i Δf t i + aiδyt i + a3iδpt i + a4 f t 1 + a5 yt 1 + a6 pt 1 vt () i= 1 i= 0 i= 0 Δf = a + Given that Pesaran et al. cointegration approach is a relatively recent evelopment in the econometric time series literature, a brief outline of this proceure is presente as follows. The bouns testing proceure is base on the F or Wal-statistics an is the first stage of the ARDL cointegration metho. Accoringly, a joint significance test that implies no cointegration hypothesis, (H 0 : a = a = a 0), against the = alternative hypothesis, (H 1 : a 4 a5 a6 0) shoul be performe for equation (). The F test use for this proceure has a non-stanar istribution. Thus, Pesaran et al. compute two sets of critical values for a given significance level with an without a time tren. One set assumes that all variables are I(0) an the other set assumes they are all I(1). If the compute F-statistic excees the upper critical bouns value, then the H 0 is rejecte. If the F-statistic falls into the bouns then the test becomes inconclusive. Lastly, if the F-statistic is below the lower critical bouns value, it implies no cointegration. This stuy, however, aopts the critical values of Narayan (005) for the bouns F-test rather than Pesaran et al. (001). As iscusse in Narayan (005) given relatively a small sample size in this stuy (36 observations), the critical values prouce by Narayan (005) are more appropriate than that of Pesaran et al. (001). Once a long-run relationship has been establishe, equation () is estimate using an appropriate lag selection criterion. At the secon stage of the ARDL cointegration proceure, it is also possible to perform a parameter stability test for the selecte ARDL representation of the error correction moel. A general error correction moel (ECM) of equation () is formulate as follows: Δf t = a m m m 0 + a1i Δf t i + aiδyt i + a3iδpt i + λ ECt 1 + μt (3) i= 1 i= 0 i= 0 where λ is the spee of ajustment parameter an EC t-1 is the resiuals that are obtaine from the estimate cointegration moel of equation (1). The Granger representation theorem suggests that there will be Granger causality in at least one irection if there exists a cointegration relationship among the variables in equation (1), proviing that they are integrate orer of one. Engle an Granger (1987) cautions that the Granger causality test, which is conucte in the firstifferences variables by means of a vector autoregression (VAR), will be misleaing in the presence of cointegration. Therefore, an inclusion of an aitional variable to the VAR system, such as the error correction term woul help us to capture the longrun relationship. To this en, an augmente form of the Granger causality test 5

7 involving the error correction term is formulate in a multivariate pth orer vector error correction moel. f t (1 L) yt pt c = c c p i= 1 (1 L) 11i 1i 31i 1i i 3i 13i 3i 33i 14i 4i 34i f t yt pt i i i λ1 + λ λ 3 [ EC ] t 1 ω1t + ωt ω 3t (4) ( 1 L) is the lag operator. EC t-1 is the error correction term, which is obtaine from the long-run relationship escribe in equation (1), an it is not inclue in equation (4) if one fins no cointegration amongst the vector in question. The Granger causality test may be applie to equation (4) as follows: i) by checking statistical significance of the lagge ifferences of the variables for each vector; this is a measure of short-run causality; an ii) by examining statistical significance of the error-correction term for the vector that there exists a long-run relationship. As a passing note, one shoul reveal that equation (3) an (4) o not represent competing error-correction moels because equation (3) may result in ifferent lag structures on each regressors at the actual estimation stage; see Pesaran et al. (001) for etails an its mathematical erivation. All error-correction vectors in equation (4) are estimate with the same lag structure that is etermine in unrestricte VAR framework; see for example, Narayan an Singh (006). This stuy utilizes the latter proceure. The existence of a cointegration erive from equation () oes not necessarily imply that the estimate coefficients are stable, as argue in Bahmani-Oskooee an Chomsisengphet (00). The stability of coefficients of regression equations are, by an large, teste by means of Chow (1960), Brown et al. (1975), Hansen (199), an Hansen an Johansen (1993). The Chow stability test requires a priori knowlege of structural breaks in the estimation perio an its shortcomings are well ocumente, see for example Gujarati (003). In Hansen (199) an Hansen an Johansen (1993) proceures, stability tests require I(1) variables an they check the long-run parameter constancy without incorporating the short-run ynamics of a moel into the testing - as iscusse in Bahmani-Oskooee an Chomsisengphet (00). Hence, stability tests of Brown et al. (1975), which are also known as cumulative sum (CUSUM) an cumulative sum of squares (CUSUMSQ) tests base on the recursive regression resiuals, may be employe to that en. These tests also incorporate the short-run ynamics to the long-run through resiuals. The CUSUM an CUSUMSQ statistics are upate recursively an plotte against the break points of the moel. Provie that the plots of these statistics fall insie the critical bouns of 5% significance, one assumes that the coefficients of a given regression are stable. These tests are usually implemente by means of graphical representation. Empirical results Annual ata over the perio were use to estimate equation () by the Pesaran et al. proceure. Data efinition an sources of ata are cite in the Appenix A. All the series in equation (1) appear to contain a unit root in their levels but stationary in their first ifferences, inicating that they are integrate at orer one i.e., I(1) an visual inspections show no structural breaks in the time series. For brevity of presentation, they are not reporte here. 6

8 Equation () was estimate in two stages. In the first stage of the ARDL proceure, the long-run relationship of equation (1) was establishe in two steps. Firstly, the orer of lags on the first ifference variables for equation () was obtaine from unrestricte VAR by means of Akaike Information Criterion (AIC) an Schwarz Bayesian Criterion (SBC). The results of this stage are not isplaye here to conserve space. Seconly, a bouns F test was applie to equation () in orer to establish a long-run relationship between the variables. Narayan an Smyth (006) presents a etaile proceure to explain if one nees to implement the bouns F test with or without a time tren. It is possible that at the en of this testing proceure, one may en up with more than one possible cointegration relationship: one with a time tren an one without a time tren. As Narayan an Smyth (006, p.116) argues that in the spirit of the bouns test, moel two with a time tren is invali because for the moel to be vali there shoul be only one longrun relationship. In orer to avoi a possible selection problem at this stage, one may follow the proceure of Bahmani-Oskooee an Goswami (003) which sequentially test the long-run cointegration relationship in equation () on the basis of ifferent lag lengths. This stuy aopts the secon approach which implicitly assumes that equation () is free from a tren ue to the ifference variables. In summary, the F tests inicate that there exists only one cointegrating relationship without a time tren in which the epenent variable is outboun tourism eman. The proceures of this stage an the results of the bouns F testing are outline an presente in the Appenix B. Given the existence of a long-run relationship, in the next step the ARDL cointegration proceure was implemente to estimate the parameters of equation () with maximum orer of lag set to to minimize the loss of egrees of freeom. This stage involves estimating the long-run an short-run coefficients of equations (1) an (). In search of fining the optimal length of the level variables of the short-run coefficients, several lag selection criteria such as R, AIC, SBC an Hannan-Quinn Criterion (HQC) were utilize at this stage. The long-run results of equation () base on several lag criteria are reporte in Panel A of Table 1 along with their appropriate ARDL moels. The results from moel selection criteria of R an AIC are ientical. Similarly, the results of SBC an HQC are exactly the same. As can be seen from Table 1, the long-run results are quite similar with regar to coefficient magnitues an statistical significance. On the basis of estimate moels, short-run income an relative price elasticities are compute, an the results are reporte in Panel B of Table 1. The short-run elasticities, as expecte, are smaller than the longrun values. However, the magnitues of elasticities are very close in all the moels. The iagnostic test results of equation () for short-run estimations are also isplaye in the respective columns of each selection criterion in Panel C of Table 1. All the estimate moels isplay the expecte signs for the regressors an they are statistically significant. All short-run moels pass a series of stanar iagnostic tests such as serial correlation, functional form, an heterosceasticity, except normality. 7

9 Table 1. ARDL results for the time span Panel A: the long-run elasticities Depenent variable f Moel Selection Criterion Regressors R AIC SBC ARDL (1,,1) ARDL (1,,1) ARDL (1,0,1) y p (17.3) * (17.3) * (17.00) * (17.00) * (1.79) ** (1.79) ** (1.58) * (1.58) * Constant (13.01) * (13.01) * (1.81) * (1.81) * Panel B: the short-run compute elasticities y p Panel C: the short-run iagnostic test statistics χ SC (1)=0.53 χ SC (1)=0.53 χ SC (1)=0.95 χ FC (1)=1.83 χ FC (1)=1.83 χ FC (1)=1.74 χ N ()=9.53 χ N ()=9.53 χ N ()=6.16 χ H (1)=1.80 χ H (1)=1.80 χ H (1)=1.75 HQC ARDL (1,0,1) χ SC (1)=0.95 χ FC (1)=1.74 χ N ()=6.16 χ H ()=1.75 Notes for Panel B: Own calculations from above moels. Notes for Panel C: The absolute value of t-ratios is in parentheses. χ SC, χ FC, χ N, an χ H are Lagrange multiplier statistics for tests of resiual correlation, functional form mis-specification, non-normal errors an heteroskeasticity, respectively. These statistics are istribute as Chi-square variates with egrees of freeom in parentheses. * an ** inicate 5 % an 10 % significance levels, respectively In search of fining the short-run ynamics of the above moels, their error-correction representations were estimate as auxiliary moels. The estimation results an the respective appropriate optimal lag length selection criteria with some selecte iagnostics are isplaye in Table. The error-correction moels were only estimate in the case of AIC an SBC since the other moel selection criteria isplaye the ientical results. Both the error-correction terms are statistically significant an their magnitues are very close to each other. Consiering the reporte iagnostic test results an the statistical significance of the coefficients estimate in the long run an short-run, on average, the AIC moel appears to be statistically more acceptable than the SBC criterion. The AIC moel performs relatively better in terms of ynamics of the short-run variables, gooness of fit an RSS values. Therefore, it is quite plausible to accept the AIC base results as the preferre moel for the evaluation of results an inference from there. The income elasticity is 1.69 in the long run an is consistent with the iea that the tourism eman is a luxury goo. The high income elasticity also inicates that income policies will have stronger impacts on the outboun tourism eman. The compute income elasticity is above unity, therefore income growth results in a more than proportional increase in outboun tourism eman. The own relative price elasticity is 0.5 in the long-run an is within the range of previous stuies. This result inicates a price inelastic eman for the outboun tourism eman, implying 8

10 that the level of outboun tourism eman cannot be regulate extensively through price policies. The error-correction term is 0.48 with the expecte sign, suggesting that when eman is above or below its equilibrium level, eman ajusts by almost 50% within the first year. The full convergence process to its equilibrium level takes after about two years. Thus, the spee of ajustment is significantly fast in the case of any shock to the outboun tourism eman equation. Table. ECM results for the time span Depenent variable Δf t Moel Selection Criterion Regressors AIC ARDL (1,,1) SBC ARDL (1,0,1) Δ y t (.68) * (5.33) * Δy t (1.61) Δ p t (1.4) (0.88) Constant (3.11) * (4.74) * EC t 1 (5.71) * (5.86) * R F-statistics DW-statistics RSS Notes: The absolute values of t-ratios are in parentheses. RSS stans for resiual sum of squares. Since the AIC an HQC criteria prouce exactly the same error correction results, the latter estimation, therefore, is not reporte here. * an ** inicate 5 % an 10 % significance levels, respectively. Having a cointegrating relationship among [f t, y t, p t ] on the basis of the results of the bouns test, the Granger causality test was conucte to equation (4) as such that only the outboun tourism eman equation was estimate with an error- correction term. However, the Granger causality tests were applie to other moels without the errorcorrection terms, since one coul not ascertain any long-run relationship for the other vectors. Table 3 summarizes the results of the long run an short-run Granger causality. Accoring to the coefficient on the lagge error-correction term, there exists a long run relationship among the variables in the form of equation (1) as the error-correction term is statistically significant, which also confirms the results of the bouns test. In the long run, income an relative prices Granger cause outboun tourism eman an the irection of causality runs interactively through the errorcorrection term from income an relative prices to outboun tourism eman. In the case of short-run causality tests, Table 3 reveals that income Granger causes outboun tourism eman because only the F statistic for the income variable is statistically significant at the 5% level in the outboun tourism eman vector. As for the short run Granger causality tests of other equations in Table 3, it seems that only causality runs from income to outboun tourist flows but there exists no other Grange cause in the system. 9

11 Table 3. Results of Granger causality F-statistics (probability) Depenent Δ f t Δ yt Δ pt EC t 1 Variable (t-statistics) Δ f t * * (0.04) (0.4) (.41) Δ y t (0.8) (0.89) Δ p t (0.90) (0.9) Causality inference : y f Notes: * inicates 5 % significance level. The probability values are in brackets. The optimal lag length is an is base on SBC. Table enables us to select the most appropriate moel of implementing the stability test for the outboun tourism eman equation. Accoring to the reporte iagnostic tests results, the AIC base error-correction moel of equation () seems to be a relatively better fit than others. Therefore, the CUSUM an CUSUMSQ stability tests were applie to the AIC base error-correction moel an the graphs representing the tests are presente in Figures 1 an. As can be seen from Figures 1 an, the plots of CUSUM an CUSUMSQ statistics are well within the critical bouns, implying that all coefficients in the error-correction moel are stable. Therefore, the preferre moel can be use for policy ecision-making purposes such that the impact of policy changes consiering income an price will not cause major istortion in the level of outboun tourism eman, since the parameters in this equation seems to follow a stable pattern uring the estimation perio. 10

12 Figure 1. Plot of CUSUM Plot of Cumulative Sum of Recursive Resiuals The straight lines represent critical bouns at 5% significance level Figure. Plot of CUSUMSQ 1.5 Plot of Cumulative Sum of Squares of Recursive Resiuals The straight lines represent critical bouns at 5% significance level 11

13 Conclusions Like many eveloping countries, Turkey has been contributing to the growth of international tourism eman. The extent of this contribution in the coming years is expecte to be more apparent once the travel restrictions such as harsh visa proceures impose on Turkish citizens are lifte in the course of Turkey s becoming a full member of the European Union. The number of Turkish people travelling abroa for holiays will ouble in less than a ecae if the current average growth rate for the outboun tourism eman hols. The outboun tourism expenitures, however, will not be a significant buren in the foreign reserves since the share of the outboun tourism expenitures in GDP is rather low 3. Thus, the tourism policies to esign to restrict the outboun tourism eman may not prouce the esire effects. In orer to measure the extent of the economic policy ecisions on the outboun tourism eman, this stuy compute the elasticities of the outboun tourism eman equation with respect to income an relative prices both in the short-run an long-run by employing the ARDL cointegration proceure. The compute ranges of elasticities are in line with the previous stuies in the tourism eman literature. As expecte, the long-run elasticities are greater than the short-run elasticities. These results may be utilize in managing the aggregate outboun tourism eman. For example, relatively low price elasticities in both the short-run an long-run inicate that the tourism policies to restrict outboun tourism eman woul not be very effective, whilst the income policies will have a stronger impact on eman. The results of augmente Granger causality test suggest that the irection of causality runs interactively through the error-correction term from income an relative prices to the outboun tourism eman in the long-run. In the short-run, causality runs only from income to outboun tourism flows. Thus, the preiction of the level of the outboun tourism eman is possible by using income an relative prices variables. The parameter stability tests of the CUSUM an CUSUMSQ reveale that the outboun tourism eman equation is stable. Therefore, the suggeste econometric moel of the outboun tourism eman may be aopte for policy simulations an forecasting purposes. 3 The preiction of the total tourist outflows from Turkey is base on the average growth rate of the outboun tourist flows, which is estimate as 9.38% per year uring It is assume that this rate hols for the next ecae. Thus, one expects over 16 millions of Turks having foreign holiays in 015. The similar preiction is reveale for the share of the outboun tourist expenitures in GDP. Therefore, this ratio is expecte to be less than % of GDP in

14 Appenix A Data efinition an sources All ata are collecte from International Financial Statistics of (IMF), Turkish Statistical Institute (TSI), an Ministry of Tourism an Culture of Turkey (MTCT), f is total tourist outflows in thousans, in logarithm. The total tourist outflows ata iscontinue in the years of 1977, 1978 an The missing years were complete by extrapolation. Sources: TSI an MTCT. y is real gross omestic prouct in thousans of Turkish Lira (TL), in logarithm. Gross omestic prouction is eflate by the consumer price inex (CPI) of 000=100. Source: IMF. p is exchange rate ajuste relative prices between USA an Turkey, which is CPIUSA measure as: p =, where CPIUSA is consumer price inex of CPITurkey ER USA, CPITurkey is consumer price inex of Turkey, an ER is the nominal exchange rates between USA an Turkey. Consumer price inexes are base on 000=100. Source: IMF. Appenix B Bouns Test for Cointegration A bouns F test was applie to equation () in orer to test the existence of a long-run relationship by using lags from two to four following Bahmani-Oskooee an Goswami (003), as they have shown that the results of this stage are sensitive to the orer of VAR. Equation () was also estimate three more times in the same way but the epenent variable each time was replace by one of the explanatory variables in search of other possible long-run relationship in any other form than it ha alreay been escribe in equation (1). Summary results of bouns tests are presente in Table 4. Table 4 inicates only one plausible long-run relationship in which f is the epenent variable. Evience of cointegration among variables also rules out the possibility of estimate relationship being spurious. Table 4. The Results of F-test for Cointegration Calculate F-statistics for ifferent lag lengths lags 4 lags 6 lags F C ( f y, p) F C ( y f, p) F C ( p f, y) The critical value ranges of F-statistics with two explanatory variables are , an at 1%, 5% an 10% level of significances, respectively. See Narayan (005), p.1988, Case III. 13

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