Modelling Australian Domestic and International Inbound Travel: a Spatial-temporal Approach

In this paper Australian domestic and international inbound travel are modelled by an anisotropic dynamic spatial lag panel Origin-Destination (OD) travel flow model. Spatial OD travel flow models have traditionally been applied in a single cross-sectional context, where the spatial structure is assumed to have reached its long run equilibrium and temporal dynamics are not explicitly considered. On the other hand, spatial effects are rarely accounted for in traditional tourism demand modelling. We attempt to address this dichotomy between spatial modelling and time series modelling in tourism research by using a spatial-temporal model. In particular, tourism behaviour is modelled as travel flows between regions. Temporal dependencies are accounted for via the inclusion of autoregressive components, while spatial autocorrelations are explicitly accounted for at both the origin and the destination. We allow the strength of spatial autocorrelation to exhibit seasonal variations, and we allow for the possibility of asymmetry between capital-city neighbours and non-capital-city neighbours. Significant temporal and spatial dynamics have been uncovered for both domestic and international tourism demand. For example we find strong seasonal temporal autocorrelations, significant trends and significant spatial autocorrelations at both the origin and the destination. Moreover, the spatial patterns are found to be most significant during peak holiday seasons. Understanding these patterns in tourist behaviour has important implications for tourism operators.

[1]  Cheng Hsiao,et al.  Estimation of Dynamic Models with Error Components , 1981 .

[2]  Haiyan Song,et al.  TOURISM DEMAND MODELLING FORECASTING. MODERN ECONOMETRIC APPROACHES , 2000 .

[3]  Lung-fei Lee,et al.  Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large , 2008 .

[4]  P. Moran Notes on continuous stochastic phenomena. , 1950, Biometrika.

[5]  Daniel Felsenstein,et al.  Spatial Vector Autoregressions , 2007 .

[6]  M. Arellano,et al.  Another look at the instrumental variable estimation of error-components models , 1995 .

[7]  George Athanasopoulos,et al.  Modelling and Forecasting Australian Domestic Tourism , 2006 .

[8]  Cheng Hsiao,et al.  Formulation and estimation of dynamic models using panel data , 1982 .

[9]  J. Paul Elhorst,et al.  Unconditional maximum likelihood estimation of dynamic models for spatial panels , 2003 .

[10]  Marc Nerlove,et al.  Formulation and Estimation of Econometric Models for Panel Data , 1992 .

[11]  L. Anselin Spatial Econometrics: Methods and Models , 1988 .

[12]  Marc Gaudry,et al.  Spatially autocorrelated errors in origin-destination models: A new specification applied to aggregate mode choice☆ , 1989 .

[13]  J. Elhorst Unconditional Maximum Likelihood Estimation of Linear and Log‐Linear Dynamic Models for Spatial Panels , 2005 .

[14]  Cheng Hsiao,et al.  Analysis of Panel Data , 1987 .

[15]  Stephen Nickell,et al.  Biases in Dynamic Models with Fixed Effects , 1981 .

[16]  Minfeng Deng,et al.  An Anisotropic Model for Spatial Processes , 2007 .

[17]  S. Poukliakova,et al.  Tourism demand modelling and forecasting: modern econometric approaches , 2001 .

[18]  M. Pesaran,et al.  Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods , 2002 .

[19]  David Allen,et al.  Modelling interstate tourism demand in Australia: A cointegration approach , 2009, Math. Comput. Simul..

[20]  Haiyan Song,et al.  Tourism demand modelling and forecasting—A review of recent research , 2008 .

[21]  J. LeSage,et al.  Spatial Econometric Modeling of Origin-Destination Flows , 2008 .

[22]  D. Bolduc,et al.  Spatial autoregressive error components in travel flow models , 1992 .

[23]  K. Ord Estimation Methods for Models of Spatial Interaction , 1975 .

[24]  Alok Bhargava,et al.  Estimating Dynamic Random Effects Models from Panel Data Covering Short Time Periods , 1983 .

[25]  Haiyan Song,et al.  Recent Developments in Econometric Modeling and Forecasting , 2005 .

[26]  J. Paul Elhorst,et al.  Specification and Estimation of Spatial Panel Data Models , 2003 .

[27]  A. Porojan,et al.  Trade Flows and Spatial Effects: The Gravity Model Revisited , 2001 .

[28]  George Athanasopoulos,et al.  Hierarchical forecasts for Australian domestic tourism , 2009 .

[29]  Badi H. Baltagi,et al.  Testing Panel Data Regression Models with Spatial Error Correlation , 2002 .