Forecasting tourism with targeted predictors in a data-rich environment

Nuno Lourenço, Carlos Melo Gouveia, António Rua

Research output: Contribution to journalArticle

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.

Original languageEnglish
JournalEconomic Modelling
DOIs
Publication statusAccepted/In press - 1 Jan 2020

Keywords

  • Factor models
  • Forecasting
  • Large datasets
  • Tourism

Fingerprint Dive into the research topics of 'Forecasting tourism with targeted predictors in a data-rich environment'. Together they form a unique fingerprint.

  • Cite this