Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/206550 
Erscheinungsjahr: 
2019
Schriftenreihe/Nr.: 
Deutsche Bundesbank Discussion Paper No. 41/2019
Verlag: 
Deutsche Bundesbank, Frankfurt a. M.
Zusammenfassung: 
We propose a novel time-varying parameters mixed-frequency dynamic factor model which is integrated into a dynamic model averaging framework for macroeconomic nowcasting. Our suggested model can efficiently deal with the nature of the real-time data flow as well as parameter uncertainty and time-varying volatility. In addition, we develop a fast estimation algorithm. This enables us to generate nowcasts based on a large factor model space. We apply the suggested framework to nowcast German GDP. Our recursive out-of-sample forecast evaluation results reveal that our framework is able to generate forecasts superior to those obtained from a naive and more competitive benchmark models. These forecast gains seem to emerge especially during unstable periods, such as the Great Recession, but also remain over more tranquil periods.
Schlagwörter: 
dynamic factor model
forecasting
GDP
mixed-frequency
model averaging
time-varying-parameter
JEL: 
C11
C32
C51
C52
C53
ISBN: 
978-3-95729-641-2
Dokumentart: 
Working Paper
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