Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/29479
Title: Tourism demand modeling : a time-varying parameter approach
Authors: Song, H 
Wong, KKF
Keywords: Demand elasticity
Kalman filter
Time-varying parameter
Tourism demand
Issue Date: 2003
Publisher: SAGE Publications
Source: Journal of travel research, 2003, v. 42, no. 1, p. 57-64 How to cite?
Journal: Journal of travel research 
Abstract: Traditional tourism demand analysis uses ordinary least squares or maximum likelihood methods to estimate demand models, assuming that the parameters of the models remain constant over the sample period. This assumption is too restrictive, as it does not allow for behavioral changes of tourists over time. This study proposes a new methodology-the time-varying parameter (TVP) approach to tourism demand modeling. This method relaxes the assumption of parameter constancy, and the behavioral change of tourists over time is traced using a statistical estimator known as a Kalman filter. The appropriateness of the TVP approach to tourism demand modeling is then tested based on a data set of the demand for Hong Kong tourism by residents from six major tourism origin countries.
URI: http://hdl.handle.net/10397/29479
ISSN: 0047-2875
EISSN: 1552-6763
DOI: 10.1177/0047287503253908
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