Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/210745
Authors: 
Cai, Michael
Del Negro, Marco
Herbst, Edward P.
Matlin, Ethan
Sarfati, Reca
Schorfheide, Frank
Year of Publication: 
2019
Series/Report no.: 
Staff Report 893
Abstract: 
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.
Subjects: 
adaptive algorithms
Bayesian inference
density forecasts
online estimation
sequential Monte Carlo methods
JEL: 
C11
C32
C53
E32
E37
E52
Document Type: 
Working Paper

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