Abstract

CORREA, Alexander. Forecasting Tourist Arrivals to Colombia from Google Trends Search. Lect. Econ. [online]. 2021, n.95, pp.105-134.  Epub Sep 29, 2021. ISSN 0120-2596.  https://doi.org/10.17533/udea.le.n95a343462.

This study examines whether the Google Trends search criteria are useful in forecasting the monthly arrival of tourists to Colombia. To this end, a baseline model that employs as a predictor the lags values of tourist arrivals is compared with two alternative specifications: (i) the baseline model augmented with monthly data from Google Trends; and (ii) the baseline model but modified with the inclusion of weekly data from Google Trends. The results show statistically significant evidence that Google Trends data provide benefits for the evaluation and prediction of tourist arrivals to Colombia. High-frequency (weekly) data adds high predictive value compared to models that use data of the same frequency (monthly). In this way, the tourism industry and those in charge of tourism public policy can rely on the predictive capacity of Google Trends data to improve their planning processes in the short and medium run.

Keywords : tourism demand; Google Trends; forecasting; Mixed Data Sampling; tourist arrivals.

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