Online Estimation of DSGE Models

Working Paper No. 26826 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...

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Bibliographic Details
Published in:NBER Working Paper Series 2020-03, p.26826
Format: Article
Language:English
Subjects:
Bayesian analysis
Economic theory
Monte Carlo simulation
Online Access:Get full text
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Summary:Working Paper No. 26826 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.
ISSN:0898-2937
DOI:10.3386/w26826