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Modeling and Forecasting Regional Tourism Demand Using the Bayesian Global Vector Autoregressive (BGVAR) Model

Assaf, A. George, Li, Gang, Song, Haiyan and Tsionas, Mike G. (2018) Modeling and Forecasting Regional Tourism Demand Using the Bayesian Global Vector Autoregressive (BGVAR) Model Journal of Travel Research.

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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.

Item Type: Article
Divisions : Faculty of Arts and Social Sciences > School of Hospitality and Tourism Management
Authors :
NameEmailORCID
Assaf, A. George
Li, GangG.Li@surrey.ac.uk
Song, Haiyan
Tsionas, Mike G.
Date : 14 March 2018
DOI : 10.1177/0047287518759226
Copyright Disclaimer : © The Author(s) 2018
Uncontrolled Keywords : Tourism demand; Forecasting; Bayesian global VAR; Impulse response analysis; Spill-over; Southeast Asia
Depositing User : Clive Harris
Date Deposited : 04 May 2018 08:17
Last Modified : 16 Jan 2019 19:09
URI: http://epubs.surrey.ac.uk/id/eprint/846353

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