Nowcasting Russian GDP in a mixed-frequency DSGE model with a panel of non-modelled variables
Eliseev A.
This study focuses on improving the accuracy of nowcasting in DSGE models. We extend one of the general equilibrium models of the Russian economy by incorporating mixed-frequency data. Specifically, we introduce an equation that links a panel of non-modelled high-frequency indicators to observable variables, whose dynamics are determined directly by the model. The out-of-sample pseudo-real-time forecasting procedure demonstrates that incorporating these additional variables enhances the accuracy of Russian GDP nowcasting using the DSGE model. This improvement makes the model’s forecasts comparable in accuracy to state-of-the-art econometric models and superior to univariate models. We also investigate the extent to which fluctuations in high-frequency indicators are associated with macroeconomic factors, as well as the economic shocks driving the explained portion of these fluctuations. While the structural interpretation of non-modelled variables is a potential strength of the model, caution is warranted due to the econometric methodology employed.