Forecasting using mixed-frequency VARs with time-varying parameters

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: ifo Working Paper ; No. 273

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Forecasting Models; Simulation Methods ; Large Data Sets: Modeling and Analysis ; Business Fluctuations; Cycles
• Subject: Time-varying parameters ; forecasting ; mixed-frequency models ; Bayesian methods

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Heinrich, Markus ; Reif, Magnus
• Event: Veröffentlichung
• (who): ifo Institute - Leibniz Institute for Economic Research at the University of Munich
• (where): Munich
• (when): 2018

Further information

• Handle: http://hdl.handle.net/10419/191278
• Last update: 15.04.2033, 11:38 PM CEST