DOI: https://doi.org/10.52903/wp2023314
FORECASTING INFLATION: THE USE OF DYNAMIC FACTOR ANALYSIS AND NONLINEAR COMBINATIONS
Stephen G. Hall
Leicester University, Bank of Greece, and Pretoria University
George S. Tavlas
Bank of Greece and the Hoover Institution, Stanford University
Yongli Wang
Birmingham University
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 work 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.
JEL-classifications: C52, C53
Keywords: forecast combinations, structural breaks, rolling windows, dynamic factor models, Kalman filter
Acknowledgments: We thank three referees for constructive comments. We have also benefited from helpful comments at a presentation made on an earlier version by participants at the May 2022 Annual International Conference on Macroeconomic Analysis and International Finance held in Crete. Maria Monopoli provided excellent research assistance.
Correspondence:
George Tavlas
Bank of Greece
21 E Venizelos Ave
Athens, 10250, Greece
Tel. no. +30 210 320 2370
Fax. no. +30 210 320 2432
Email: gtavlas@bankofgreece.gr