Online Estimation of DSGE Models / Michael D. Cai, Marco Del Negro, Edward P. Herbst, Ethan Matlin, Reca Sarfati, Frank Schorfheide.

Format:

Book

Author/Creator:

Cai, Michael D.

Contributor:

National Bureau of Economic Research.

Del Negro, Marco.

Herbst, Edward P.

Matlin, Ethan.

Sarfati, Reca.

Schorfheide, Frank.

Series:

Working Paper Series (National Bureau of Economic Research) no. w26826.

NBER working paper series no. w26826

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2020.

Summary:

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.

Notes:

Print version record

March 2020.