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 that allow for both structural change and indicators sampled at different frequencies. We extend the literature by evaluating a mixed‐frequency time‐varying parameter vector autoregressive model with stochastic volatility. Monte Carlo simulation shows that the novel model is well‐suited to estimate missing monthly observations in an environment that is subject to parameter instability. In a real‐time forecast exercise, the model delivers accurate now‐ and forecasts and, on average, outperforms its competitors. Particularly, inflation and unemployment rate forecasts are more precise.