Online Estimation of DSGE Models

Michael Cai, Marco Del Negro, Edward Herbst, Ethan Matlin, Reca Sarfati and Frank Schorfheide
Additional contact information
Ethan Matlin: https://economics.harvard.edu/people/ethan-matlin

No 2020-023, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

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, document the accuracy and runtime benefits o fgeneralized data tempering for “online” estimation (that is, re-estimating a model asnew data become available), 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 and study the sensitivity ofthe predictive performance to changes in the prior distribution. We find that making priors less informative (compared to the benchmark priors used in the literature) by increasing the prior variance does not lead to a deterioration of forecast accuracy.

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: 55 p.
Date: 2020-02-28
New Economics Papers: this item is included in nep-dge, nep-for, nep-gen, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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https://www.federalreserve.gov/econres/feds/files/2020023pap.pdf (application/pdf)

Related works:
Journal Article: Online estimation of DSGE models (2021)
Working Paper: Online Estimation of DSGE Models (2020)
Working Paper: Online Estimation of DSGE Models (2019)
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:fip:fedgfe:2020-23

DOI: 10.17016/FEDS.2020.023

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