Online Estimation of DSGE Models

Michael Cai (), Marco Del Negro, Edward Herbst, Ethan Matlin (), Reca Sarfati and Frank Schorfheide
Additional contact information
Michael Cai: Northwestern University
Ethan Matlin: FRB New York

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

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 that are commonly adopted in the literature.

Keywords: Adaptive algorithms; Bayesian inference; density forecasts; online estimation; sequential Monte Carlo methods (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 E32 E37 E52 (search for similar items in EconPapers)
Pages: 70 pages
Date: 2019-07-22
New Economics Papers: this item is included in nep-dge, nep-ecm, nep-for, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Online Estimation of DSGE Models (2020)
Working Paper: Online Estimation of DSGE Models (2020)
Working Paper: Online Estimation of DSGE Models (2019)
Working Paper: Online Estimation of DSGE Models (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:19-014

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