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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 variousmodels and putting forward some general conclusions.
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