UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods o¤er an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors re‡ecting di¤erent blocks of variables (e.g. a price block, a housing block, a …nancial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for di¤erent parsimonious forecasting models to hold at di¤erent points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to di¤erent forecasting model as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output and in‡ation using 139 UK monthly time series variables, we …nd that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods. Keywords: Bayesian, state space model, factor model, dynamic model averaging JEL Classi…cation: E31, E37, C11, C53 Both authors are Fellows at the Rimini Centre for Economic Analysis. We would like to thank George Kapetanios and the Bank of England Econometrics group for their help and the provision of the data.