A Frequency-Domain Analysis Of Medium-Scale Dsge Models

This paper employs the local Bayesian likelihood methodology to estimate a medium-scale dynamic stochastic general equilibrium (DSGE) model on different frequencies and uses frequency-domain tools to evaluate the time-varying parameter model and the fixed-parameter model. These techniques yield fresh insights into theoretical and empirical implications conveyed by alternative models beyond what conventional time-domain approaches can offer. We show that parameter estimates are sensitive to frequencies, and goodness-of-fit varies substantially with the frequency bands. Overall, the estimated time-varying parameter model captures the properties of the U.S. data better in the business cycle frequency band, and beyond this band, the fixed-parameter model performs better. Additionally, our study also reveals the importance of structural shocks in improving the fit between models and data. Finally, we utilize the spectral representation of generalized forecast error variance decomposition to investigate the frequency dynamics of volatility connectedness. We find shocks to economic activity have an impact on variables at different frequencies with different strengths, and markets become more connected during crisis periods. Furthermore, this study provides insights into a question policymakers are much concerned with: which shocks are major sources of economic volatilities and which sectors serve as major recipients of shocks?