Forecasting tourist arrivals using time-varying parameter structural time series models

Empirical evidence has shown that seasonal patterns of tourism demand and the effects of various influencing factors on this demand tend to change over time. To forecast future tourism demand accurately requires appropriate modelling of these changes. Based on the structural time series model (STSM)...

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Bibliographic Details
Published in:International journal of forecasting 2011-07, Vol.27 (3), p.855-869
Main Authors: Song, Haiyan, Li, Gang, Witt, Stephen F., Athanasopoulos, George
Format: Article
Language:English
Subjects:
Seasonality
State space models
Statistical analysis
Stochastic
Tourism demand forecasting
Tourism demand forecasting Seasonality Stochastic State space models
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Summary:Empirical evidence has shown that seasonal patterns of tourism demand and the effects of various influencing factors on this demand tend to change over time. To forecast future tourism demand accurately requires appropriate modelling of these changes. Based on the structural time series model (STSM) and the time-varying parameter (TVP) regression approach, this study develops the causal STSM further by introducing TVP estimation of the explanatory variable coefficients, and therefore combines the merits of the STSM and TVP models. This new model, the TVP-STSM, is employed for modelling and forecasting quarterly tourist arrivals to Hong Kong from four key source markets: China, South Korea, the UK and the USA. The empirical results show that the TVP-STSM outperforms all seven competitors, including the basic and causal STSMs and the TVP model for one- to four-quarter-ahead ex post forecasts and one-quarter-ahead ex ante forecasts.
ISSN:0169-2070
1872-8200
DOI:10.1016/j.ijforecast.2010.06.001