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Working Paper
The financial accelerator mechanism: does frequency matter?
We use mixed-frequency (quarterly-monthly) data to estimate a dynamic stochastic general equilibrium model embedded with the financial accelerator mechanism à la Bernanke et al. (1999). We find that the financial accelerator can work very differently at monthly frequency compared to quarterly frequency; that is, we document its inversion. That is because aggregating monthly data into quarterly data leads to large biases in the estimated quarterly parameters and, as a consequence, to a deep change in the transmission of shocks.
Report
Exploiting the monthly data flow in structural forecasting
This paper develops a framework that allows us to combine the tools provided by structural models for economic interpretation and policy analysis with those of reduced-form models designed for nowcasting. We show how to map a quarterly dynamic stochastic general equilibrium (DSGE) model into a higher frequency (monthly) version that maintains the same economic restrictions. Moreover, we show how to augment the monthly DSGE with auxiliary data that can enhance the analysis and the predictive accuracy in now-casting and forecasting. Our empirical results show that both the monthly version of ...