Forecasting inflation: The use of dynamic factor analysis and nonlinear combinations

Stephen G. Hall, George S. Tavlas*, Yongli Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This paper considers the problem of forecasting inflation in the United States, the euro area, and the United Kingdom in the presence of possible structural breaks and changing parameters. We examine a range of moving window techniques that have been proposed in the literature. We extend previous works by considering factor models using principal components and dynamic factors. We then consider the use of forecast combinations with time-varying weights. Our basic finding is that moving windows do not produce a clear benefit to forecasting. Time-varying combination of forecasts does produce a substantial improvement in forecasting accuracy.
Original languageEnglish
Pages (from-to)514-529
Number of pages16
JournalJournal of Forecasting
Volume42
Issue number3
Early online date22 Jan 2023
DOIs
Publication statusPublished - Apr 2023

Keywords

  • dynamic factor models
  • forecast combinations
  • Kalman filter
  • rolling windows
  • structural breaks

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