Forecasting tourism with targeted predictors in a data-rich environment

Nuno Lourenço, Carlos Melo Gouveia and António Rua

Economic Modelling, 2021, vol. 96, issue C, 445-454

Abstract: Along with the deepening of globalization and economic integration, economic agents face the challenge on how to extract useful information from large panels of data for forecasting purposes. Herein, we lay out a modelling strategy to explore the predictive content of large datasets for tourism forecasting. In particular, we assess the role of multi-country datasets to nowcast and forecast tourism by resorting to factor models with targeted predictors to cope with such a data-rich environment. Drawing on business and consumer surveys for Portugal and its main tourism source markets, we document the usefulness of factor models to forecast tourism exports up to several months ahead. Moreover, we find that forecast performance is enhanced if predictors are chosen before factors are estimated.

Keywords: Forecasting; Tourism; Factor models; Large datasets (search for similar items in EconPapers)
JEL-codes: C53 C55 F47 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:96:y:2021:i:c:p:445-454

DOI: 10.1016/j.econmod.2020.03.030

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