Forecasting tourism with targeted predictors in a data-rich environment

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.