Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1124
Title: Bayesian models for tourism demand forecasting
Authors: Wong, KF
Song, H 
Chon, KKS 
Issue Date: Oct-2006
Source: Tourism management, Oct. 2006, v. 27, no. 5, p. 773-780
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.
Keywords: Forecasting performance
Vector autoregressive process
Over parameterization
Bayesian approach
Publisher: Pergamon Press
Journal: Tourism management 
ISSN: 0261-5177
EISSN: 1879-3193
DOI: 10.1016/j.tourman.2005.05.017
Rights: Tourism Management © 2006 Elsevier Ltd. The journal web site is located at http://www.sciencedirect.com.
Appears in Collections:Journal/Magazine Article

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