Online estimation of DSGE models - Michael Cai; Marco Del Negro; Edward P. Herbst; Ethan Matlin; Reca Sarfati; Frank Schorfheide (Federal Reserve Bank of New York, 2019)
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 call generalized tempering for "online" estimation, and provide examples of multimodal posteriors that are well captured by SMC methods. We then use the online estimation of the DSGE model to compute pseudo-out-of-sample density forecasts of DSGE models with and without financial frictions and document 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 use priors that are substantially looser than those commonly adopted in the literature.
Keywords: C11; C32; C53; E32; E37; E52; 330; adaptive algorithms; Bayesian inference; density forecasts; online estimation; sequential Monte Carlo methods