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Forecasting using mixed-frequency VARs with time-varying parameters

Markus Heinrich and Magnus Reif ()

No 273, ifo Working Paper Series from ifo Institute - Leibniz Institute for Economic Research at the University of Munich

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.

Keywords: Time-varying parameters; forecasting; mixed-frequency models; Bayesian methods (search for similar items in EconPapers)
JEL-codes: C11 C53 C55 E32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-mac and nep-ore
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
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