Search query and tourism forecasting during the pandemic: When and where can digital footprints be helpful as predictors?

Yang Yang, Yawen Fan, Lan Jiang and Xiaohui Liu

Annals of Tourism Research, 2022, vol. 93, issue C

Abstract: During the COVID-19 pandemic, daily tourism demand forecasting provides actionable insight on tourism operations amid intense uncertainty. This paper applies the lasso method to predict daily tourism demand across 74 countries in 2020. We evaluate the usefulness of online search queries in boosting forecasting accuracy. The lasso method is used to select appropriate predictors and their lag orders. Results indicate that, in general, no evidence supports the usefulness of Google Trends data in generating more accurate forecasts. However, in some countries, the data can be useful for reducing the forecasting errors. Regression analysis further demonstrates that the relative usefulness of online search queries is associated with pandemic severity, the dominance of inbound tourism, and island geography. Lastly, implications are provided.

Keywords: Tourism forecasting; Lasso method; Google trends; COVID-19 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:anture:v:93:y:2022:i:c:s0160738322000160

DOI: 10.1016/j.annals.2022.103365

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