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, Koon-foo
Song, Haiyan
Chon, Kaye Kye-sung
Subjects: Forecasting performance
Vector autoregressive process
Over parameterization
Bayesian approach
Issue Date: Oct-2006
Publisher: Elsevier Ltd.
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.
Rights: Tourism Management © 2006 Elsevier Ltd. The journal web site is located at http://www.sciencedirect.com.
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/1124
DOI: 10.1016/j.tourman.2005.05.017
ISSN: 0261-5177
Appears in Collections:SHTM Journal/Magazine Articles

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