Tourism demand modeling: A time-varying parameter approach

Haiyan Song, Kevin K.F. Wong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

129 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)57-64
Number of pages8
JournalJournal of Travel Research
Issue number1
Publication statusPublished - 1 Jan 2003


  • Demand elasticity
  • Kalman filter
  • Time-varying parameter
  • Tourism demand

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Transportation
  • Tourism, Leisure and Hospitality Management


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