NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH
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Online Estimation of DSGE Models

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

NBER Working Paper No. 26826
Issued in March 2020
NBER Program(s):Economic Fluctuations and Growth, Monetary Economics

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.

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Document Object Identifier (DOI): 10.3386/w26826

 
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