Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/191278
Authors: 
Heinrich, Markus
Reif, Magnus
Year of Publication: 
2018
Series/Report no.: 
Ifo Working Paper 273
Abstract: 
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.
Subjects: 
Time-varying parameters
forecasting
mixed-frequency models
Bayesian methods
JEL: 
C11
C53
C55
E32
Document Type: 
Working Paper
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