A mixed frequency vector autoregression (MFVAR) model is proposed for nowcasting, forecasting and backcasting Australian macroeconomic indicators at monthly and quarterly frequencies. A novel augmented Minnesota prior for MFVAR models is also introduced. This prior ensures that a priori regularisation applied to the autoregression coefficients in the MFVAR is equivalent to that of a VAR model estimated using the coarsest frequency data. The model provides monthly estimates of CPI and GDP from January 1991 to December 2023, which have historically been released only at a coarser quarterly frequency. In an in‐sample analysis, we demonstrate that these higher frequency indexes offer credible advantages over existing methods. The MFVAR also provides competitive point and density forecasts of four key macroeconomic indicators—CPI, GDP, the cash rate and the unemployment rate—compared to a quarterly VAR model, while also providing higher frequency monthly forecasts of all variables.
Trinh et al. (Tue,) studied this question.