Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/191278 
Erscheinungsjahr: 
2018
Schriftenreihe/Nr.: 
ifo Working Paper No. 273
Verlag: 
ifo Institute - Leibniz Institute for Economic Research at the University of Munich, Munich
Zusammenfassung: 
We extend the literature on economic forecasting by constructing a mixed-frequency time-varying parameter vector autoregression with stochastic volatility (MF-TVP-SVVAR). The latter is able to cope with structural changes and can handle indicators sampled at different frequencies. We conduct a real-time forecast exercise to predict US key macroeconomic variables and compare the predictions of the MF-TVP-SV-VAR with several linear, nonlinear, mixed-frequency, and quarterly-frequency VARs. Our key finding is that the MF-TVPSV-VAR delivers very accurate forecasts and, on average, outperforms its competitors. In particular, inflation forecasts benefit from this new forecasting approach. Finally, we assess the models' performance during the Great Recession and find that the combination of stochastic volatility, time-varying parameters, and mixed-frequencies generates very precise inflation forecasts.
Schlagwörter: 
Time-varying parameters
forecasting
mixed-frequency models
Bayesian methods
JEL: 
C11
C53
C55
E32
Dokumentart: 
Working Paper

Datei(en):
Datei
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