Bayesian models for tourism demand forecasting

Research output: Journal article publicationJournal articleAcademic researchpeer-review

71 Citations (Scopus)

Abstract

This study extends the existing forecasting accuracy debate in the tourism literature by examining the forecasting performance of various vector autoregressive (VAR) models. In particular, this study seeks to ascertain whether the introduction of the Bayesian restrictions (priors) to the unrestricted VAR process would lead to an improvement in forecasting performance in terms of achieving a higher degree of accuracy. The empirical results based on a data set on the demand for Hong Kong tourism show that the Bayesian VAR (BVAR) models invariably outperform their unrestricted VAR counterparts. It is noteworthy that the univariate BVAR was found to be the best performing model among all the competing models examined.
Original languageEnglish
Pages (from-to)773-780
Number of pages8
JournalTourism Management
Volume27
Issue number5
DOIs
Publication statusPublished - 1 Oct 2006

Keywords

  • Bayesian approach
  • Forecasting performance
  • Over parameterization
  • Vector autoregressive process

ASJC Scopus subject areas

  • Development
  • Transportation
  • Tourism, Leisure and Hospitality Management
  • Strategy and Management

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