Online Estimation of DSGE Models

Michael D. Cai, Marco Del Negro, Edward Herbst, Ethan Matlin, Reca Sarfati and Frank Schorfheide

No 26826, NBER Working Papers from National Bureau of Economic Research, Inc

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 of generalized data tempering for “online” estimation (that is, re-estimating a model as new 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 of the 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.

JEL-codes: C11 C32 C53 E32 E37 E52 (search for similar items in EconPapers)
Date: 2020-03
New Economics Papers: this item is included in nep-dge, nep-ets, nep-for, nep-mac and nep-ore
Note: EFG ME
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Published as Michael Cai & Marco Del Negro & Edward Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2021. "Online estimation of DSGE models," The Econometrics Journal, vol 24(1), pages C33-C58.

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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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