Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/89470
| 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. |
| Appears in Collections: | Journal/Magazine Article |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| 15. BGVAR_Assaf_et_al.pdf | Pre-Published version | 1.59 MB | Adobe PDF | View/Open |
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