The specification of a tourism demand model now commonly includes lagged demand as an explanatory variable. This raises issues in the formulation and interpretation of econometric tourism demand models. It is argued that a simple lagged demand term is not sufficient to account for the dynamics of tourism demand. Many alternative specifications of the dynamic elements and form have been used in tourism demand modelling. This paper helps to resolve the issue of how the dynamics should be specified. It does this by a thorough review of the dynamic elements relevant to tourism demand and by demonstrating that the specification does make a difference in practice. Consideration of different formulations of the dynamics of tourism demand, drawing on theory and specific characteristics of tourism, leads to the recommendation of an ARIMA with explanatory variables model form.
ChoV. (2001), ‘Tourism forecasting and its relationship with leading economic indicators’, Journal of Hospitality and Tourism Research, Vol 25, No 4, pp 399–420.
3.
CroesR.R.VanegasM. (2005), ‘An econometric study of tourist arrivals in Aruba and its implications’, Tourism Management, Vol 26, No 6, pp 879–890.
4.
CrouchG.I.ShawR.N. (1990), ‘Determinants of international tourist flows: Findings from 30 years of empirical research’, Management Papers No 29, Graduate School of Management, Monash University, Melbourne.
5.
DarnellA.C.JohnsonP.S. (2001), ‘Repeat visits to attractions: A preliminary economic analysis’, Tourism Management, Vol 22, pp 119–126.
6.
DivisekeraS.KulendranN. (2006), ‘Economic effects of advertising on tourism demand: A case study’, Tourism Economics, Vol 12, pp 187–205.
7.
DritsakisN. (2004), ‘Cointegration analysis of German and British tourism demand for Greece’Tourism Management, Vol 25, pp 111–119.
8.
EngleR.F.GrangerC.W.J. (1987), ‘Co-integration and error correction: Representation, estimation and testing’, Econometrica, Vol 55, pp 251–276.
9.
FujiE.T.MakJ. (1981), ‘Forecasting tourism demand: Some methodological issues’, Annals of Regional Science, Vol 15 (July), pp 72–82.
10.
GalletC.A.BraunB.M. (2001), ‘Gradual switching regression estimates of tourism demand’, Annals of Tourism Research, Vol 28, pp 503–507.
11.
Garín-MuñozT. (2007), ‘German demand for tourism in Spain’, Tourism Management, Vol 28, pp 12–22.
12.
GeorgeB.P.GeorgeB.P. (2004), ‘Past visits and the intention to revisit a destination: Place attachment as the mediator and novelty seeking as the moderator’, Journal of Tourism Studies, Vol 15, No 2, pp 51–66.
13.
KozakM. (2001), ‘Repeater's behaviour at two distinct destinations’, Annals of Tourism Research, Vol 28, No 3, pp 784–807.
14.
KulendranN.DivisekeraS. (2004), ‘The estimation of marketing expenditure elasticities’, in ChinK.HsuC.H.C.OkamotoN., eds, Globalisation and Tourism Research: East Meets West, Asia Pacific Tourism Association, Hong Kong, pp 325–331.
15.
KulendranN.WittS.F. (2003), ‘Forecasting the demand for international business travel’, Journal of Travel Research, Vol 41, pp 261–271.
16.
LehtoX.Y.O'LearyJ.T.MorrisonA.M. (2004), ‘The effect of prior experience on vacation behaviour’, Annals of Tourism Research, Vol 31, No 4, pp 801–818.
17.
LiG.SongH.WittS.F. (2005), ‘Recent developments in econometric modelling and forecasting’, Journal of Travel Research, Vol 44, pp 82–99.
18.
LiG.WongK.K.F.SongH.WittS.F. (2006), ‘Tourism demand forecasting: A time varying parameter error correction model’, Journal of Travel Research, Vol 45, pp 175–185.
19.
MakJ.MoncurJ.YonamineD. (1977), ‘Determinants of visitor expenditures and visitor lengths of stay: A cross-section analysis of US visitors to Hawaii’, Journal of Travel Research, Vol 15, No 3, pp 5–8.
20.
MorleyC.L. (1991), ‘Modeling international tourism demand: Model specification and structure’, Journal of Travel Research, Vol 30, No 1, pp 40–44.
21.
MorleyC.L. (1992), ‘A micro-economic theory of international tourism demand’, Annals of Tourism Research, Vol 19, pp 250–267.
22.
MorleyC.L. (1996), ‘A comparison of three methods for estimating tourism demand models’, Tourism Economics, Vol 2, pp 223–234.
23.
MorleyC.L. (1998), ‘A dynamic international demand model’, Annals of Tourism Research, Vol 25, No 1, pp 250–267.
24.
MorleyC.L. (2000), ‘Demand modelling methodologies: Integration and other issues’, Tourism Economics, Vol 6, No 1, pp 5–20.
25.
OhC.-O.MorzuchB.J. (2005), ‘Evaluating time-series models to forecast the demand for tourism in Singapore: Comparing within-sample and postsample results’, Journal of Travel Research, Vol 43, pp 404–413.
26.
PetrickJ.F. (2004), ‘First timers’ and repeaters’ perceived value’, Journal of Travel Research, Vol 43, pp 29–38.
SalmanA.K. (2003), ‘Estimating tourist demand through cointegration analysis: Swedish data’, Current Issues in Tourism, Vol 6, No 4, pp 323–339.
29.
SmeralE.WügerM. (2005), ‘Does complexity matter? Methods for improving forecasting accuracy in tourism: The case of Austria’, Journal of Travel Research, Vol 44, pp 100–110.
30.
SongH.TurnerL. (2006), ‘Tourism demand forecasting’, in DwyerL.ForsythP., eds, International Handbook on the Economics of Tourism, Edward Elgar, Cheltenham, pp 89–114.
31.
SongH.WittS.F. (2003), ‘Tourism forecasting: The general-to-specific approach’, Journal of Travel Research, Vol 42, No 1, pp 65–74.
32.
SongH.WongK.K.F. (2003), ‘Tourism demand modelling: A time-varying parameter approach’, Journal of Travel Research, Vol 42, pp 57–67.
33.
WebberA.G. (2001), ‘Exchange rate volatility and cointegration in tourism demand’, Journal of Travel Research, Vol 39, pp 398–405.
34.
WittS.F.MartinC.A. (1987), ‘Econometric models for forecasting international tourism demand’, Journal of Travel Research, Vol 29, No 3, pp 23–30.
