Macroeconometric forecasting using a cluster of dynamic factor models

Christian Glocker and Serguei Kaniovski ()
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Serguei Kaniovski: Austrian Institute of Economic Research

Empirical Economics, 2022, vol. 63, issue 1, No 2, 43-91

Abstract: Abstract We propose a modeling 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 (search for similar items in EconPapers)
JEL-codes: C22 C53 C55 E37 (search for similar items in EconPapers)
Date: 2022
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Working Paper: Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models (2020)
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DOI: 10.1007/s00181-021-02129-w

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