Tourism Demand Modelling and Forecasting
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ADLM
analysis
Category=KNSG
Cointegrating Vectors
cointegration
Cointegration Relationship
demand elasticity modelling
econometric methods
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Error Correction Model
explanatory
FE Model
forecasting accuracy measures
Forecasting Error Variance Decomposition
General ADLM
Heterogeneous Intercepts
hypothesis
International Tourism Spending
Johansen VECM
Kalman filter estimation
lag
lengths
LR Statistic
OLS Estimate
OLS Residual
panel data techniques
quantitative tourism demand forecasting
relationship
SE Shock
Short Run Error Correction Model
time series analysis
Tourism Demand Model
Tourism Demand Variable
TVP Approach
TVP Model
UK Model
UK Resident
UK USA
Unrestricted Var Model
USA Model
Var Model
variables
vector
Product details
- ISBN 9780080436739
- Weight: 510g
- Dimensions: 156 x 234mm
- Publication Date: 30 Jun 2000
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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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 various models and putting forward some general conclusions.
Haiyan Song, Stephen F. Wittpson
