Online estimation of DSGE models

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

The Econometrics Journal, 2021, vol. 24, issue 1, C33-C58

Abstract: SummaryThis paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating dynamic stochastic general equilibrium (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 with 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)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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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)
Working Paper: Online Estimation of DSGE Models (2019)
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