Online Estimation of DSGE Models


This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, explore the benefits of an SMC variant we devise termed generalized tempering for “online” estimation, and provide examples of multimodal posteriors well-captured by SMC methods. We then use our online estimation technique to compute pseudo-out-of-sample density forecasts of DSGE models with and without financial frictions, documenting the benefits of conditioning DSGE model forecasts on nowcasts of macroeconomic variables and interest rate expectations. We also study whether the predictive ability of DSGE models changes when we adopt priors substantially looser than commonly adopted in the literature.

The Econometrics Journal (conditionally accepted)

Presented at:

Ph.D. Student in Economics and Statistics

I am an Economics Ph.D. candidate at MIT, and former Senior Research Analyst on the dynamic stochastic general equilibrium (DSGE) team in the Macroeconomic and Monetary Studies function at the NY Fed. Views expressed are my own.