Structural Modeling and Forecasting Using a Cluster of Dynamic Factor Models

40 Pages Posted: 18 Aug 2020

See all articles by Christian Glocker

Christian Glocker

Austrian Institute of Economic Research (WIFO)

Serguei Kaniovski

Austrian Institute of Economic Research

Date Written: July 16, 2020

Abstract

We propose a modeling approach involving a series of small-scale dynamic factor models. They are connected to each other within a cluster, whose linkages are derived from Granger-causality tests. This approach merges the benefits of large-scale macroeconomic and small-scale factor models, rendering our Cluster of Dynamic Factor Models (CDFM) useful for model-consistent nowcasting and forecasting on a larger scale. While the CDFM has a simple structure and is easy to replicate, its forecasts are more precise than those of a wide range of competing models and those of professional forecasters. Moreover, the CDFM allows forecasters to introduce their own judgment and hence produce conditional forecasts.

Keywords: Forecasting, Dynamic factor model, Granger causality, Structural modeling

JEL Classification: C22, C53, C55, E37

Suggested Citation

Glocker, Christian and Kaniovski, Serguei, Structural Modeling and Forecasting Using a Cluster of Dynamic Factor Models (July 16, 2020). Available at SSRN: https://ssrn.com/abstract=3652959 or http://dx.doi.org/10.2139/ssrn.3652959

Christian Glocker (Contact Author)

Austrian Institute of Economic Research (WIFO) ( email )

Arsenal, Objekt 20
Vienna, 1030
Austria

Serguei Kaniovski

Austrian Institute of Economic Research

P.O. Box 91
Wien, A-1103
Austria

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