Tourism Demand Modelling and Forecasting
Modern Econometric ApproachesEdited By
- . Haiyan Song, School of Management Studies for the Service Sector, University of Surrey, Guildford, Surrey GU2 5XH, UK
- S.F. Witt, School of Management Studies for the Service Sector, University of Surrey, Guildford, Surrey GU2 5XH, UK
The phenomenal growth of both the world-wide tourism industry and academic interest in tourism over the last thirty years has generated great interest in tourism demand modelling and forecasting from both sectors. However, the tendency for researchers and practitioners engaged in quantitative causal tourism modelling and forecasting to run many regression equations and try to choose the 'best' model based on various parametric and non-parametric criteria has been widely criticised as failing to provide credible results. The aim of this book is to present the recent advances in econometric modelling methodology within the context of tourism demand analysis at a level that is accessible to non-specialists, and to illustrate these new developments with actual tourism applications.
The book begins with an introduction to the fundamentals of tourism demand analysis, before addressing the problems of traditional tourism demand modelling and forecasting, i.e. data mining and spurious regression due to common trends in the time series. Three chapters explore the general-to-specific approach to tourism demand modelling and forecasting, including the use of autoregressive distributed lag processes, cointegration analysis and error correction models. The time varying parameter model together with the use of the Kalman filter as an estimation method is a useful tool for examining the effects of regime shifts on tourism demand elasticities: this is explored next. The panel data approach is introduced as a way of overcoming the problem of estimation and forecasting biases caused by insufficient time series data. The book concludes by evaluating the empirical forecasting performance of the various models and putting forward some general conclusions.
For final-year undergraduate, taught postgraduate and research students in tourism studies, as well as researchers and practitioners who wish to apply the recent advances in econometric modelling and forecasting to tourism demand analysis.
Published: June 2000
Tourism Demand Modelling and Forecasting: Modern Econometric Approaches makes a welcome and timely contribution in updating the existing methodology on tourism demand modelling and forecasting. It serves as a useful reference for research students and academics in tourism as well as industry practitioners in the field who are interested in applying modern econometric techniques to the analysis of tourism demand.
Kevin K.F. Wong, Asia Pacific Journal of Tourism Research
This book is a very timely description and application of advanced causal forecasting methods for tourism data series....Overall, this book is very important in raising the level of technical expertise required to achieve accurate causal model forecasts, and explaining which this is necessary. As a text, the book pushed the understanding of tourism forecasting methodology out to currently accepted econometric standards, and must become a standard research reference work for economic forecasting of tourism series.
Lindsay Turner, Tourism Management
...provides a very comprehensive and concise analysis of major econometric issues in the context of tourism demand....it is of great value to advanced students, academic researcher and practitioners who focus explicitly on quantitative methodologies in Information Technology and Tourism.
Andreas Papatheodorou, University of Surrey, UK, Information Technology in Tourism Journal, Vol 4, Nos 3-4
- Preface. Introduction to Tourism Demand Analysis. Introduction. Determinants of tourism demand. Functional form and demand elasticities. Summary. Traditional Methodology of Tourism Demand Modelling. Introduction. Traditional methodology of tourism demand modelling. Failure of the traditional approach to tourism demand modelling. General-to-Specific Modelling. Introduction. General-to-specific modelling. Tests of restrictions on regression parameters. Diagnostic checking. Model selection. Worked example. Cointegration. Spurious regression. The concept of cointegration. Test for order of integration. Test for Cointegration. Error Correction Model. Cointegration and error correction mechanism. Estimating the ECM. Worked example. Vector Autoregression (VAR) and Cointegration. Introduction. Basics of VAR analysis. Impulse response analysis. Forecasting with VAR and error variance decomposition. Testing for Granger causality and exogeneity. An example of VAR modelling. VAR modelling and cointegration. Time Varying Parameter Modelling. Introduction. Are tourism demand elasticities constant over time? The TVP model and Kalman filter. A worked example. Summary and conclusions. Panel Data Analysis. Introduction. Model specification and estimation. An empirical study of a tourism demand model based on the panel data approach. Summary and conclusions. Evaluation of Forecasting Performance. Introduction. Measures of forecasting accuracy. Forecasting evaluation of different models. Concluding remarks. References. Author index. Subject index.