Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/89470
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Title: Modeling and forecasting regional tourism demand using the Bayesian Global Vector Autoregressive (BGVAR) model
Authors: Assaf, AG
Li, G
Song, H 
Tsionas, MG
Issue Date: 1-Mar-2019
Source: Journal of travel research, 1 Mar. 2019, v. 58, no. 3, p. 383-397
Abstract: Increasing levels of global and regional integration have led to tourist flows between countries becoming closely linked. These links should be considered when modeling and forecasting international tourism demand within a region. This study introduces a comprehensive and accurate systematic approach to tourism demand analysis, based on a Bayesian global vector autoregressive (BGVAR) model. An empirical study of international tourist flows in nine countries in Southeast Asia demonstrates the ability of the BGVAR model to capture the spillover effects of international tourism demand in this region. The study provides clear evidence that the BGVAR model consistently outperforms three other alternative VAR model versions throughout one- to four-quarters-ahead forecasting horizons. The potential of the BGVAR model in future applications is demonstrated by its superiority in both modeling and forecasting tourism demand.
Keywords: Bayesian global VAR
forecasting
impulse response analysis
Southeast Asia
Spillover
tourism demand
Publisher: SAGE Publications
Journal: Journal of travel research 
ISSN: 0047-2875
EISSN: 1552-6763
DOI: 10.1177/0047287518759226
Rights: This is the accepted version of the publication Assaf, A. G., Li, G., Song, H., & Tsionas, M. G., Modeling and forecasting regional tourism demand using the bayesian global vector autoregressive (BGVAR) model, Journal of Travel Research (Volume 58 and issue 3) pp. 383-397. Copyright © 2018 (The Author(s)). DOI: 10.1177/0047287518759226.
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