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Modeling and Forecasting Monthly Tourism Arrivals to Aruba Since COVID-19 Pandemic, Olga Bespalova

Abstract
This paper improves short-term forecasting models of monthly tourism arrivals by estimating and evaluating a time-series model with exogenous regressors (ARIMA-X) using a case of Aruba, a small open tourism-dependent economy. Given importance of the US market for Aruba, it investigates informational value of Google Searches originating in the USA, flight capacity utilization on the US air-carriers, and per capita demand of the US consumers, given the volatility index in stock markets (VIX). It yields several insights. First, flight capacity is the best variable to account for the travel restrictions during the pandemic. Second, US real personal consumption expenditure becomes a more significnat predictor than income as the former better captured impact of the COVID-19 restrictions on the consumers’ behavior, while income boosted by the pandemic fiscal support was not fully directed to spending. Third, intercept correction improves the model in the estimation period. Finally, the pandemic changed econometric relationships between the tourism arrivals and their main determinants, and accuracy of the forecast models. Going forward, the analysts should re-estimate the models. Out-of-sample forecasts with 5 percent confidence intervals are produced for 18 months ahead
Language
eng
Literary Form
non fiction
Physical Description
1 online resource (38 pages)
Form Of Item
online

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