Hall, Stephen GeorgeTavlas, George S.Wang, Yongli2023-06-012023Hall, S. G., Tavlas, G. S.,& Wang, Y. (2023). Forecasting inflation: The useof dynamic factor analysis and nonlinearcombinations.Journal of Forecasting,42(3),514–529.https://doi.org/10.1002/for.2948.0277-6693 (print)1099-131X (online)10.1002/for.2948http://hdl.handle.net/2263/91002DATA AVAILABILITY STATEMENT : All data are taken from publicly available data sources as defined in the data appendix. The particular vintage of data used in this study is available upon request from the authors.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.en© 2023 John Wiley & Sons, Ltd. This is the pre-peer reviewed version of the following article : (name of article), Journal name, vol. , no. , pp. , 2022, doi : . The definite version is available at : http://wileyonlinelibrary.com/journal/for [24 months embargo]Dynamic factor modelsForecast combinationsKalman filterRolling windowsStructural breaksSDG-08: Decent work and economic growthPostprint Article