Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters

This paper provides a detailed assessment of the real-time forecast accuracy of a wide range of vector autoregressive models (VAR) that allow for both structural change and indicators sampled at different frequencies. We extend the literature by evaluating a mixed-frequency time-varying parameter VAR with stochastic volatility (MF-TVP-SV-VAR). Overall, the MF-TVP-SV-VAR delivers accurate now- and forecasts and, on average, outperforms its competitors. We assess the models' accuracy relative to expert forecasts and show that the MF-TVP-SV-VAR delivers better inflation nowcasts in this regard. Using an optimal prediction pool, we moreover demonstrate that the MF-TVP-SV-VAR has gained importance since the Great Recession.