The Relaonsh beween Macroeconomcs and Oubound Toursm Evdence from Tawan Dr. Mng-Che Chou, Asssan Professor, Toursm Managemen Dearmen, Shh Hsn Unversy, Tawan ABSTRACT Ths arcle dscusses he shor-erm and long-erm effecs ha he ndusral roducon nde, consumer rce nde and foregn echange rae have on he erson-mes of oubound ravelers n he ravel model. Ths research was underaken usng he Johansen s conegraon es mehodology o analyze he daa. In addon, hs research also emloys he Imulse Resonse Funcon o analyze he mac of cross-erod dynamc beween varables. The fndngs of hs research show ha asde from a sgnfcan shor-erm ransory mac, he ndusral roducon nde also causes a ransory mac on he erson-mes of oubound ravelers n he long-erm. Keywords: erson-me of oubound ravelers, macroeconomc varables, Imulse Resonse Funcon FOREWORD In recen years, here has been much leraure dscussng he neracon beween oursm and macroeconomc varables. Leraure focusng on research no he relaonsh beween he echange rae and oursm nclude Lorde (995), who found durng he forecas of demand n he Carbbean ha he weghed average echange nde s he man facor nfluencng he number of nbound oursm n ha area, whle Kana (999) argeed research n Canada, nvesgang he rlaeral relaonsh when comarng he Canadan dollar o he US dollar, Brsh serlng, and Jaanese yen. Ths research showed ha he nfluence of Brsh serlng and Jaanese yen on he Canadan dollar was more ronounced whle ha of he US dollar was less sgnfcan. Furhermore, Shachmurove (999) analyzed he nfluence ha he black marke and offcal echange markes have on oursm. Maeo (999) dscussed he nfluence ha echange raes have on Canadan ravelers by car o he Uned Saes, whle Coshall () used me seres analyss o research he oenal nfluence ha echange raes, beween Brsh serlng, he US dollar and French francs, have on he eendures of Brsh ravelers. There s also research on he relaonsh beween consumer rce and oursm. Marn and Cooer (999) analyzed he mac ha New Zealand arlne cke rces have on he arlne ndusry. Lndberg and Aylward (999) dscussed he relaonsh beween rce elascy, commody rces and oursm n 3 naonal arks n Cosa Rca. Research on hs oc also ncludes Paaheodorou (999), Kashya and Bojanc (), and Goodrch (). Research on he relaonsh beween ncome levels and oursm nclude Agarwal and Yochum (999); Cromon, Lee and Shuser () ha argeed ours aracons n he Uned Saes; and Baajens and Njkam () whch focused on he regonal vewon of he Greek slands. For research on he relaonsh beween a arcular counry s ncome level and oursm, here s Greg (999), who comared Euroean counres, he Uned Saes and Jaan; Vcurne () focused on Uganda; Goldsen () researched he Afrcan counry Wesern Sahara; and, Srzz and Mes () who argeed Lan Amercan counres and he Carbbean, LAC. Furhermore, here s research ha combnes several macroeconomc varables n her analyss of oursm. Quayson and Var (985) used he ravel demand model o esmae he oursm demand of New York Cy. They found ha he ncome level of local ravelers n New York Cy were less elasc and sensve, whle Canadan ravelers were eremely sensve even o very small changes n he echange rae beween he US dollar and Canadan dollar. Ths research arges oubound ravelers from Tawan. Breakng away from sngle varable mac, he research dscusses he nfluences ha Tawan s ndusral roducon nde, consumer rce nde and echange rae flucuaon have on he number of oubound ravelers. The resul of hs research wll be rovded o he oursm ndusry and oursm relaed governmen agences for reference. In order o dscuss he long-erm and shor-erm neracons of The Journal of Inernaonal Managemen Sudes, Volume 5, Number, Arl, 65
dfferen varables, hs research fully combned many me seres analyses ha were develoed n recen years, ncludng:. Johansen s VAR Model (988,99, &994), whch ncororaes he conegraon es o deermne f hese varables have a long-erm balancng relaonsh. The un-roo es was also ncororaed o ake accoun of he varable s saonary as a rerequse for he conegraon es (hs arcle wll ado he ADF un-roo es from Dckey & Fuller (98)).. Granger (988) s ECM Model o erform he Granger Causaly es on shor-erm neracon. 3. Generalzed Imulse Resonse Funcon o evaluae he cross-erod dynamc effec of 4 varables. 4. The use of Varance Decomoson o deermne he eogeney orderng of hese varables. The urose of combnng hese me seres analyses s o erform n-deh research on he neracon beween hese varables n he shor-erm, and he mac on he long-erm equlbrum, mac and changes. Mehodology and Emrcal Resuls Usng he ravel ndusry of Tawan as evdence, hs aer nvesgaes he dynamc relaonsh among he number of oubound ourss and hree macroeconomc fundamenals, namely ndusral roducon, he consumer rce nde, and he echange rae. The ravel model can be formulaed as: TRO = f ( IP, CPI, EX ) () where TRO reresens he number of oubound ourss. IP, CPI, and EX are he symbols for ndusral roducon, he consumer rce nde, and he echange rae. However, for he long-erm co-movemen, TRO ogeher wh IP, CPI, and EX formulae a muually endogenous VAR model. X = Π + Π X + Π X +... + Π X + () where X s a 4 vecor, whch ncludes he varables of TRO, IP, CPI, and EX. Π s a 4 vecor of nerce. Π s a 4 4 coeffcen mar. s a 4 vecor of error erm. In order o avod he surous regresson roblem and o fully nvesgae he long-erm equlbrum and shor-erm dynamc relaonsh beween he varables of our ravel model, varous me seres mehodologes are emloyed n hs sudy, whch ncludes Johansen (988 and 994) and Johansen and Jueslus s (99) fve VAR model conegraon (CI) ess for esng he long run equlbrum relaonsh, Granger s (969) Granger causaly (GC) es for esng for he lead-lag relaonsh, he generalzed-mulse resonse funcon (G-IRF) o evaluae he neracve mulse effec, and varance decomoson (VDC) o nvesgae he relave eogeney. Un Roos: Accordng o Schwer (989) and Aya and Burrdge (), hs aer emloys ADF es o es for he saonary of each varable of our ravel model. The ADF s hree models are eressed as he followng forms: y = φ y + β y + (3) y = α + φy + β y + (4) y = α + γ + φy + β y + (5) Equaon (3) s a ure random walk wh he lag erms. Equaon (4) ossesses a drf. Equaon (5) ncludes a drf and a me rend. The null hyohess for he ADF es s: H : φ =, wh he alernave H : - < φ <. Elder and Kennedy () argued ha a sraegy s necessary o deermne whch of he hree ADF models should be emloyed n conducng he un roo es. In hs aer, we follow he deermnng rule of Doldado, Jenknson, and Sosvlla-Rvero (DJS) (99) o deermne he arorae model for each rae. Moreover, snce he esmaon mgh be based f he lag lengh s re-desgnaed whou rgorous deermnaon, hs aer ados he Schwarz Bayesan nformaon creron (SBC) o selec he omal number of lags based on he rncle of arsmony. 66 The Journal of Inernaonal Managemen Sudes, Volume 5, Number, Arl,
Table resens he resuls of he ADF ess ha each varable has s un-roo n he level and s rejeced o be non-saonary n he frs dfference. Ths ensures he I() ye seres for all four varables are consdered n our ravel model. Table : The resuls of ADF un-roo ess (erod 98/-/) Level Frs dfference τ ( ) τ ( ) τ ( ) τ ( ) τ ( ) τ ( ) τ IP.53[4] -.96[4] -.95[] -6.899[] -6.953[] -6.93[] EX -.8[7] -.546[7] -.95[7] -3.455[7] -3.448[7] -3.53[7] CPI.455[4] -.85[4] -.965[3] -4.46[3] -8.3[] -8.4[] TRO.33[5].3[5] -.473[4] -.84[] -.84[] -.43[] noes:. IP, EX, CPI, and TRO reresen ndusral roducon, echange rae, consumer rce nde, and oubound oursm, resecvely.. The symbol,, and, reresen he sgnfcan a %, 5%, and % levels, resecvely. 3. τ(), τ (), and τ τ() are he es sascs for a un roo n he level whou consan, wh consan, and wh boh consan and rend, resecvely. 4. τ(), τ (), and τ τ() are he es sascs for a un roo n he dfference whou consan, wh consan, and wh boh consan and rend, resecvely. 5. The crcal values (-3.9935; -3.47; -3.368) for he ADF -sascs are from he MacKnnon (996) able. 6. The bold numbers ndcae he arorae model of ADF deermned by DJS (99). 7. The numbers whn he square bracke are he arorae lag lenghs for each varable based on MAIC. Conegraon Varous mehods of esmang conegraon have been aled o caure he long-erm equlbrum relaonsh beween he varables. Among hese, Johansen s mehodology, based on he lkelhood rao wh non-sandard asymoc dsrbuons nvolvng negrals of Brownan moon, s found o be he bes mehod o roceed wh conegraon esmaon by Gonzalo (994). () The elaborae works develoed by Johansen (988) and furher eended by Johansen and Jueslus (99) and Johansen (994) are summarzed no fve VAR models wh ECM, whch are resened n he followng forms: 988: H ( r ) : X = Γ X +... + Γk X k+ + αβ X + ψd (6) r 99: H ( ) : X 99: H ( ) : X r ( r 994: H ) : X 994: H ( ) : X r X X k+ + ( β, β )( X, ) + ψd X X k+ + αβ X + + ψd X X k+ + ( β, β)( X, ) + X X k + + αβ X + + + ψd = Γ... α (7) = Γ... (8) = Γ... α + ψd (9) = Γ... () Johansen (994) emhaszed he role of he deermnsc erm, Y = +, whch ncludes consan and lnear erms n he Gaussan VAR. Followng Neh and Lee s () decson rocedure among he hyoheses H(r) and H(r) for fve dfferen models, he arorae conegraon relaonsh can be found n he resence of lnear rend and quadrac rend. () When esng for four-varable VAR, he frs model wh wo conegraon ranks s found (see Table ). Ths mles ha TRO co-moves wh our hree resumed macroeconomc fundamenals n he long run. Ths long-erm equlbrum relaonsh beween he varables demonsraes a ure co-movemen resenng neher lnear rend nor quadrac rend. Table : Deermnaon of Conegraon rank n he Presence of a Lnear Trend and a Quadrac Trend Model Model Model 3 Model 4 Model 5 H (5) H (5) H (5) H (5) H (5) Rank T (r) C (5%) T (r) C (5%) T (r) C (5%) T (r) C (5%) T (r) C (5%) r = r r.9 39.89 56.7 53. 38.8 47. 86.37 6.99 8.8 54.64 r 3 7.8 4.3 53.8 34.9 4.35 9.68 8.99 4.44 78.53 34.55 9.48.53 9.54 9.96 8.44 5.4.58 5.3 8.34 8.7.8 3.84.33 9.4. 3.76 3.8.5 3.8 3.74 noes:. T (r), T (r), T (r), T (r), and T (r) denoe he LR es sascs for all he null of H(r) versus he alernave of H() of Johansen s fve models. τ The Journal of Inernaonal Managemen Sudes, Volume 5, Number, Arl, 67
. C (5%), C (5%), C (5%), C (5%) and C (5%) are 5% LR crcal values for Johansen s fve models, whch are eraced from Oserwald-Lenum (99). 3. The model selecon follows Neh and Lee s () decson rocedure, dagnosng models one by one unl he model ha canno be rejeced for he null. 4. The bold number wh underlne ndcaes he selecon of he rank n he resence of lnear rend and quadrac rend. 5. VAR lengh seleced based on he smalles number of SBC s 5 for all he models, as ndcaed n he arenhess. Snce he urose of hs sudy s o fnd he degree of nfluence of each macroeconomc fundamenal over he TRO, we furher es for he arwse conegraon relaonsh beween each of he resumed macroeconomc varables and he TRO. From Table-3,we observe ha CPI s he only varable found o share he long-run equlbrum relaonsh wh he TRO of Tawan. The rce level n Tawan seems he mos nfluenal varable as an ndcaor o redc he long-erm oubound oursm movemen. However, analyzng he shor-erm macs of he conegraon equaons, we fnd ha all hree coeffcens are shown o be sgnfcan. The -values are 9.76, 3.9, and 6.35 resecvely, for he macs of ndusral roducon, echange rae and consumer rce nde on he TRO. However, all he nfluences are shown o be negave, whch mles ha he decrease n he level of ndusral roducon, he arecaon of he Tawan dollar, and he fall of he consumer rce nde wll ncrease he TRO. Table 3: Conegraon es beween oubound oursm and each of macroeconomc varables TRO and IP []: Conegraon equaon: TRO = 46. IP ( 4. 84 ) Rank T (r) C T (r) C T (r) C T (r) C T (r) C r.9.53 59.3 9.96 56.8 5.4.65 5.3.65 8.7. 3.84 3.9 9.4.85 3.76 34.6.5 34.6 3.74 r = TRO and EX []: Conegraon equaon: TRO = 7. 58EX ( 3. 45) Rank T (r) C T (r) C T (r) C T (r) C T (r) C r 5.9.53 9.65 9.96 9. 5.4 49.5 5.3 47.79 8.7. 3.84.58 9.4.39 3.76.88.5.46 3.74 r = TRO and CPI []: Conegraon equaon: TRO = 884. 4CPI ( 89. 6 ) Rank T (r) C T (r) C T (r) C T (r) C T (r) C r 7.34.53 8.99 9.96 66.88 5.4 77.7 5.3 75.5 8.7 3.4 3.84. 9.4.9 3.76 5.84.5 4.5 3.74 r = noes:.. IP, EX, CPI, and TRO reresen ndusral roducon, echange rae, consumer rce nde, and oubound oursm, resecvely.. T (r), T (r), T (r), T (r), and T (r) denoe he LR es sascs for all he null of H(r) versus he alernave of H() of Johansen s fve models. 3. C (5%), C (5%), C (5%), C (5%) and C (5%) are 5% LR crcal values for Johansen s fve models, whch are eraced from Oserwald-Lenum (99). 4. The model selecon follows Neh and Lee s () decson rocedure, dagnosng models one by one unl he model ha canno be rejeced for he null. 5. The bold number wh underlne ndcaes he selecon of he rank n he resence of lnear rend and quadrac rend. 6. VAR lengh seleced based on he smalles number of SBC s for all he models, as ndcaed n he square bracke. Granger Causaly To es he lead-lag relaonsh beween arwse varables, Granger (969) develoed an nfluenal echnque, called he Granger causaly es. Consderng wo seres, A and B, he models elaned n he form of Granger (969) are as follows: k A = c + α A + β B + k a () 68 The Journal of Inernaonal Managemen Sudes, Volume 5, Number, Arl,
k B = c + α B + β A + k b () where k s he lag lengh and s seleced by SBC n hs sudy. The null s ha he seres B fals o Granger cause A f β = (,,3,,k) and he seres A fals o cause B f β =. Table 4: Parwse Granger causaly es Null Hyohess F-Sasc Probably IP does no Granger Cause TRO TRO does no Granger Cause IP 56.455 83.53.. EX does no Granger Cause TRO TRO does no Granger Cause EX.6358 9.8784.564.93 CPI does no Granger Cause TRO TRO does no Granger Cause CPI 4.4989 7.57..4 EX does no Granger Cause IP IP does no Granger Cause EX.679.95388.89.633 CPI does no Granger Cause IP IP does no Granger Cause CPI 57.979.33.. CPI does no Granger Cause EX EX does no Granger Cause CPI 7.3398 3.7987.78.554 noes:. TRO, IP, EX and CPI are he symbols for oubound ours, ndusral roducon, echange rae and consumer rce nde, resecvely.. The symbol,, and, reresen he sgnfcan a %, 5%, and % levels, resecvely. 3. The null hyohess, H, s for "no causal relaon". 4. Lag lengh s seleced by SBC. Table 4 reresens he resuls of he GC es for our oubound oursm relaonsh. As shown, he echange rae s he only facor whch does no lead he rend of he TRO, whereas ndusral roducon and consumer rce nde boh show srong feedback relaonshs wh he TRO. Varance Decomoson and Generalzed-Imulse Resonse Funcon Followng Sms (98, 986) and Hamlon (994), he reduced form of he srucure VAR model: B = Γ + Γ +, can be ransformed o a four-dmensonal sandard form: = A + A + e, = A B Γ are a 4 vecor of consans; Γ, A = B Γ and he back oeraor B are 4 4 where Γ and marces; he whe- nose,, and he dsurbance e = B are 4 vecors. For furher dervaon, we oban a vecor movng average (VMA) reresenaon: n n = + A e [.e., = ( I + A + + A ) A + n+... A e A ] + In order o ransfer he model o be eressed n he form of whe-nose dsurbance, we fnally reach he form as he followng eresson: where jk s a 4 vecor of consans and elemens of jk () n = + φ ( ) (3) φ, a 4 4 mar wh φ () jk =I 4, are he mac mullers, whch eamne he neracon over he enre ah of volaly, eor, mor and roducvy sequences. Equaon (3) s he so-called mulse resonse funcon. If he dsurbance a all lags,, are absoluely and conemoraneously uncorrelaed, we can easly fnd he ercenage of he FEV ha occurs n he VAR, and hen judge he relave eogeney of all he resumed endogenous varables. However, s no always he case. Researchers hus aled Cholesk decomoson (.e., mully he dsurbance erm, by a 4 4 lower rangular mar V, where VV =I 4 ) o consruc a VMA reresenaon wh a dsurbance rocess ha s orhogonal conemoraneously a all lags. The Journal of Inernaonal Managemen Sudes, Volume 5, Number, Arl, 69
Assume he VMA reresenaon: = α + C, where C s a 4 4 mar wh I 4 = ransformaon of hs VMA n erms of orhogonal nnovaons a all lags s gven by where D = α + CVV = + CV = V. = and α D (4) From he equaon (4), he k-se ahead forecas error of s gven by: E k = D T + D +... + Dk k + where E k D[,,...] resen value of. as follows: E (5) = k, k a k C =. The, mles ha ulzng all he nformaon se a erod -k o forecas he The corresondng varance-covarance mar of hs k-se ahead of forecas error s eressed E k k = D ( ) ( ) ( ) E D + DE D +... + Dk E Dk E (6) As Kng e al. (99) and Zhou (996) on ou ha as here are more han one common rends n a model, dfferen orderng of varables may sgnfcanly affec he resuls of IRF and VDC f he common rends are no absoluely uncorrelaed. In hs aer, he relave eogeney of our four varables s ordered, from he above GC es, as heconsumer rce nde, TRO, ndusral roducon and echange rae (.e., CPI TRO IP EX). Snce he echnque of varance decomoson (VDC) decomoses he forecas error varance (FEV), whch n urn offers nformaon abou he relave morance of each random nnovaon o he varables. We, based on he resumed eogeney orderng, furher nvesgae he forecas error varance decomoson of our oubound oursm model. CONCLUSION Ths sudy, usng Tawan s ravel ndusry as evdence, ams a deermnng he facors whch affec he number of oubound ourss of Tawan. We frs fnd ha a long-run equlbrum relaonsh ess among he numbers of oubound ourss and hree resumed macroeconomc fundamenals, namely IP, CPI, and ER. However, CPI s he only varable found o share he long-erm equlbrum relaonsh wh he oubound oursm of Tawan when dual esng s used. Analyzng he shor-run mac of he conegraon equaon, we fnd ha all hree fundamenals are shown o affec he TRO sgnfcanly and negavely, whch mles ha economc growh, nflaon and currency deresson wll all reduce he TRO. Furher esng of he Granger causaly fnds feedback relaonshs beween he TRO and each of IP and CPI, bu no he EX. The forecas error varance of each varable s mosly self-elaned and no elaned by oher varables. However, n elanng he forecas error varance of he TRO, IP urns ou o be he only facor whch shows moderae elanng ower n he long-run. The generalzed-mulse resonse funcon reveals ha, n he shor run, he number of oubound ourss s no only self-resonded, bu also resonded ransorly and negavely o he shock of ndusral roducon. However, he resonses of he number of oubound ourss o shocks of all macroeconomc fundamenals are dmnshed n he long run. I can be concluded ha here s no ermanen mac of nnovaons of macroeconomc fundamenals on he number of oubound ourss. NOTES () Gonzalo (994) comared several mehods of esmang conegraon, whch nclude ordnary leas squares, nonlnear leas squares, he mamum lkelhood n an error correcon model, rncle comonens, and canoncal correlaons. 7 The Journal of Inernaonal Managemen Sudes, Volume 5, Number, Arl,
() Neh and Lee s () decson rocedure s ndeed an alcaon of esng rocedure develoed by Johansen (99, 994) based on he deas of Panula s (989) nes and non-nes hyohess o deermne he number of conegrang rank n he resence of lnear rend [Johansen (99)] and quadrac rend [Johansen (994)]. The decson rocedure s organzed n he followng way: H () H () H () H () H () H () H () H () H () H ()...... H (-) H (-) H (-) H (-) H (-) REFERENCES Agarwal, Vnod B and Glber R Yochum(999), Tours sendng and race of vsors, Journal of Travel Research, 38(), 73-76 Coshall, John T. (), Secral analyss of overseas ourss eendures n he Uned Kngdom, Journal of Travel Research, 38(3), 9-98 Cromon, John L, Seokho Lee and Thomas J Shuser(), A gude for underakng economc mac sudes: The Srngfes eamle, Journal of Travel Research, 4(), 79-87 Dckey, D. A. and W. A. Fuller (98), Lkelhood rao sascs for auoregressve me seres wh a un roo, Economerca, 49, 57-7 Dolado, J, T. Jenknson, and S. Sosvlla-Rvero (99), Conegraon and un roos, Journal of Economc Surveys, 4, 49-73 Ello, G., T. J.Rohenberg, and J. H. Sock (996), Effcen ess for an auoregressve un roo, Economerca, 64, 83-836 Engle, R. and C. Granger (987), Co-negraon and error correcon reresenaon, esmaon and esng, Economerca, 55, 5-67 Goldsen, Andrea (), Infrasrucure develomen and regulaory reform n sub-saharan Afrca: The case of ar ransor, The World Economy, 4(), -48 Goodrch, Jonahan N. (), Toursm and develomen n mounan regons, Journal of Travel Research, 3(9), 468-469 Johansen, S. (988), Sascal analyss of conegraon vecors, Journal of Economc Dynamcs and Conrol,, 3-54 Johansen, S (994), The role of he consan and lnear erms n conegraon analyss of nonsaonary varables, Economerc Revews, 3, 5-9 Johansen, S. and Juselus, K. (99), Mamum lkelhood esmaon and nference on conegraon wh alcaons o he demand for money, Oford Bullen of Economcs and Sascs, 5,69- Kashya, Rajv and Davd C Bojanc(), A srucural analyss of value, qualy, and rce erceons of busness and lesure ravelers, Journal of Travel Research, 39(), 45-5 Lm, Chrsne and Mchael McAleer(), Conegraon analyss of quarerly oursm demand by Hong Kong and Sngaore for Ausrala, Aled Economcs, 33(), 599-69 Lorde, Corne Elane (995), Inernaonal and Carbbean oursm managemen hrough forecasng echnques, Unublshed Ph.D. dsseraon, Walden Unversy, Mnneaols, MN. Marn and Cooer (999), Oubound ravel and qualy of lfe, Journal of busness research, 44(3), 79-88 Maeo, Lvo D (999), Usng alernave mehods o esmae he deermnans of cross-border rs, Aled Economcs, 3(), 77-88 Oserwald-Lenum, M. (99), Praconers corner- a noe wh quanles of he asymoc dsrbuon of he mamum lkelhood conegraon rank es sascs, Oford Bullen of Economcs and Sascs, 54,46-47 Paaheodorou, Andreas (999), The demand for nernaonal oursm n he Mederranean regon, Aled Economcs, 3(5), 69-63 Pesaran, M. H., and Y. Shn, (998), Generalzed mulse resonse analyss n lnear mulvarae models, Economc Leers, 58, 7-9. Pesaran, M. H., Y. Shn, and R. J. Smh (), Bounds esng aroaches o he analyss of long-run relaonsh, Journal of Aled Economercs, 6, 89-36 Phlls, P.C.B. and P. Perron (988), Tesng for a un roo n me seres regresson, Bomerka, 75, 335-346 Quayson, J. and T. Var (985), The muller mac of oursm n he Okanagan, Annals of Toursm Research,, 497-54 Srzz, Ncolno and Sco Mes(), Challenges facng oursm markes n Lan Amerca and he Carbbean regon n he new mllennum, Journal of Travel Research, 4(), 83-9 The Journal of Inernaonal Managemen Sudes, Volume 5, Number, Arl, 7