Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models

Christian Glocker and Serguei Kaniovski

No 614, WIFO Working Papers from WIFO

Abstract: We propose a modelling approach involving a series of small-scale factor models. They are connected to each other within a cluster, whose linkages are derived from Granger-causality tests. GDP forecasts are established across the production, income and expenditure accounts within a disaggregated approach. This method merges the benefits of large-scale macroeconomic and small-scale factor models, rendering our Cluster of Dynamic Factor Models (CDFM) useful for model-consistent forecasting on a large scale. While the CDFM has a simple structure, its forecasts outperform those of a wide range of competing models and 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 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2020-10
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Journal Article: Macroeconometric forecasting using a cluster of dynamic factor models (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:wfo:wpaper:y:2020:i:614

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