Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/227497 
Erscheinungsjahr: 
2020
Schriftenreihe/Nr.: 
WIFO Working Papers No. 614
Verlag: 
Austrian Institute of Economic Research (WIFO), Vienna
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.
Schlagwörter: 
Forecasting
Dynamic factor model
Granger causality
Structural modeling
JEL: 
C22
C53
C55
E37
Dokumentart: 
Working Paper

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