Tourism demand modelling and forecasting : modern econometric approaches / Haiyan Song and Stephen F. Witt.

Includes bibliographical references (pages 167-171) and indexes.

Print version record.

Determinants of Tourism Demand -- Functional Form and Demand Elasticities -- Traditional Methodology of Tourism Demand Modelling -- Traditional Methodology of Tourism Demand Modelling -- Failure of the Traditional Approach to Tourism Demand Modelling -- General-to-Specific Modelling -- Tests of Restrictions on Regression Parameters -- Diagnostic Checking -- Model Selection -- 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 -- Vector Autoregression (VAR) and Cointegration -- 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 -- Are Tourism Demand Elasticities Constant Over Time? -- The TVP Model and Kalman Filter -- Panel Data Analysis -- Model Specification and Estimation -- An Empirical Study of a Tourism Demand Model Based on the Panel Data Approach -- Evaluation of Forecasting Performance -- Measures of Forecasting Accuracy -- Forecasting Evaluation of Different Models.

"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. Finally, the book concludes by evaluating the empirical forecasting performance of the various models and putting forward some general conclusions."--Jacket.

English.

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