Scenario Forecasting for Global Tourism


This study provides innovative forecasts of the probabilities of certain scenarios of tourism demand. The scenarios of interest are constructed in relation to tourism growth and economic growth. The probability forecasts based on these scenarios provide valuable information for destination policy makers. The time-varying parameter panel vector autoregressive (TVP-PVAR) model is adopted for scenario forecasting. Both the accuracy rate and the Brier score are used to evaluate the forecasting performance. A global set of 25 tourism destinations is empirically examined, and the results confirm that the TVP-PVAR model with a time-varying error covariance matrix is generally a promising tool for forecasting. Our study contributes to tourism forecasting literature in advocating the use of scenario forecasting to facilitate industry decision making in situations wherein forecasts are defined by two or more dimensions simultaneously. In addition, it is the first study to introduce the TVP-PVAR model to tourism demand forecasting.

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Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Economics, Finance & Entrepreneurship
College of Business and Social Sciences > Aston Business School > Centre for Personal Financial Wellbeing
Additional Information: © Sage 2020. The final publication is available via Sage at
Uncontrolled Keywords: Brier score,economic growth,scenario forecasting,time-varying parameter panel vector autoregressive,tourism growth,Education,Tourism, Leisure and Hospitality Management
Publication ISSN: 1096-3480
Last Modified: 18 Jun 2024 07:27
Date Deposited: 08 Jun 2020 10:32
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Related URLs: https://journal ... 096348020919990 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021-01
Published Online Date: 2020-06-04
Accepted Date: 2020-02-17
Authors: Wu, Doris Chenguang
Cao, Zheng (ORCID Profile 0000-0002-3545-7313)
Wen, Long
Song, Haiyan



Version: Accepted Version

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