SummaryIn order to simultaneously consider mixed-frequency time series their joint dynamics and possible structural change we introduce a time-varying parameter mixed-frequency vector autoregression (VAR). Time variation enters in a parsimonious way: only the intercepts and a common factor in the error variances can vary. Computational complexity therefore remains in a range that still allows us to estimate moderately large VARs in a reasonable amount of time. This makes our model an appealing addition to any suite of forecasting models. For eleven U.S. variables we show the competitiveness compared to a commonly used constant-coefficient mixed-frequency VAR and other related model classes. Our model also accurately captures the drop in the gross domestic product during the COVID-19 pandemic.
Bayesian methods ; time-varying intercepts ; common stochastic volatility ; forecasting ; real-time data ; COVID-19 case study (search for similar items in EconPapers)