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Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models
We propose a modelling approach based on a set of small-scale factor models linked together in a cluster with linkages derived from Granger causality tests. GDP forecasts are produced using a disaggregated approach across production, expenditure and income accounts. The method combines the advantages of large structural macroeconomic models and small factor models, making our cluster of dynamic factor models (CDFM) useful for large-scale model-consistent forecasting. The CDFM has a simple structure, and its forecasts outperform those of a variety of competing models and professional forecasters. In addition, the CDFM allows forecasters to use their own judgment to produce conditional forecasts.
Keywords:Forecasting, Dynamic factor model, Granger causality, Structural modeling
Forschungsbereich:Makroökonomie und europäische Wirtschaftspolitik