Forecasting Inflation: The Use of Dynamic Factor Analysis and Nonlinear Combinations

Stephen Hall, George Tavlas and Yongli Wang
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Yongli Wang: University of Birmingham

Discussion Papers from Department of Economics, University of Birmingham

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.

Keywords: forecast combinations; structural breaks; rolling windows; dynamic factor models; Kalman filter (search for similar items in EconPapers)
JEL-codes: C52 C53 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2022-10
New Economics Papers: this item is included in nep-ban, nep-ets, nep-for and nep-mon
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https://repec.cal.bham.ac.uk/pdf/22-12.pdf

Related works:
Journal Article: Forecasting inflation: The use of dynamic factor analysis and nonlinear combinations (2023)
Working Paper: Forecasting inflation: the use of dynamic factor analysis and nonlinear combinations (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:bir:birmec:22-12

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