Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models
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 app...| Link(s) zu Dokument(en): | WIFO Publikation |
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| Veröffentlicht in: | WIFO Working Papers |
| Hauptverfasser: | , |
| Format: | paper |
| Sprache: | Englisch |
| Veröffentlicht: |
2020
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| Zusammenfassung: | 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. |
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