The good, the bad and the ugly on COVID-19 tourism recovery

Anestis Fotiadis, Stathis Polyzos and Tzung-Cheng T.C. Huan

Annals of Tourism Research, 2021, vol. 87, issue C

Abstract: This paper is to produce different scenarios in forecasts for international tourism demand, in light of the COVID-19 pandemic. By implementing two distinct methodologies (the Long Short Term Memory neural network and the Generalized Additive Model), based on recent crises, we are able to calculate the expected drop in the international tourist arrivals for the next 12 months. We use a rolling-window testing strategy to calculate accuracy metrics and show that even though all models have comparable accuracy, the forecasts produced vary significantly according to the training data set, a finding that should be alarming to researchers. Our results indicate that the drop in tourist arrivals can range between 30.8% and 76.3% and will persist at least until June 2021.

Keywords: Coronavirus; Tourism demand; Deep learning; Generalized additive model; Pandemia (search for similar items in EconPapers)
JEL-codes: H12 P46 Z32 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (57)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:anture:v:87:y:2021:i:c:s0160738320302619

DOI: 10.1016/j.annals.2020.103117

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