Dating and synchronizing tourism growth cycles
Author links open overlay panel Jorge M. Information for Authors Editors Librarians Societies. The cyclical components of the series are extracted using the structural time series framework by Harvey, , and the interrelations between the variables are evaluated using innovation accounting.
The authors use the non-parametric method proposed by Harding and Pagan (2003) to date tourism growth cycles. This study is among the first to use robust, transparent.
We found that German tourism plays a leading role, since its movements are followed with delays by tourism flows from other countries, and exhibits higher resilience to shocks.
Journal of China Tourism Research. The Case of Taiwan. Forecasting Structural Time Series Models and the Kalman Filter. Empirical Evidence from the Computable General Equilibrium Model.
How to identify the growth stages of wheat
Vinod, Handbook of Statistics, Vol. Dating and Synchronizing Tourism Growth Cycles. He is also the author of several publications in influential international journals. Tourism Demand Modeling and Forecasting - Modern Econometric Approaches. If you would like to, you can learn more about the cookies we use. Investigating Causal Relations by Econometric Methods and Cross-Spectral Methods.
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Effects of the Hodrick-Prescott Filter on Trend and Difference Stationary Time Series: Investigating Causal Relations by Econometric Methods and Cross-Spectral Methods.
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Analysis of Tourism Trends in Spain. Journal of China Tourism Research.
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Tourism Demand Modeling and Forecasting - Modern Econometric Approaches. Turner and Stephen F.