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Predicting Recessions in (almost) Real Time in a Big-data Setting

Author

Listed:
  • Alexandre Bonnet R. Costa
  • Pedro Cavalcanti G. Ferreira
  • Wagner Piazza Gaglianone
  • Osmani Teixeira C. Guillén
  • João Victor Issler
  • Artur Brasil Fialho Rodrigues

Abstract

The objective of this paper is to propose an approach for dating recessions in real time (or slightly a posteriori) that is suitable to a big data environment. Our proposal is to mix the canonical correlation approach of Issler and Vahid (2006) with the big data approach defended by Stock and Watson (2014). We incorporate the good elements of each approach into one. This involves solving both the problem of missing data and high dimensionality in big databases, besides defining a decision rule on how to choose the best forecasting model in real time. Our empirical results show it is possible to track the state of the U.S. and European economies using the models developed here, as long as appropriate techniques to reduce the dimensionality of the databases are implemented - canonical correlations coupled with principal component analysis. Depending on the cutoffs chosen, the models predict recessions in real time with an accuracy of 98% and 80%, respectively, for the U.S. and the Euro Area.

Suggested Citation

  • Alexandre Bonnet R. Costa & Pedro Cavalcanti G. Ferreira & Wagner Piazza Gaglianone & Osmani Teixeira C. Guillén & João Victor Issler & Artur Brasil Fialho Rodrigues, 2023. "Predicting Recessions in (almost) Real Time in a Big-data Setting," Working Papers Series 587, Central Bank of Brazil, Research Department.
    • Handle: RePEc:bcb:wpaper:587
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    References listed on IDEAS

    as
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