Working Paper: NBER ID: w26826
Authors: Michael D. Cai; Marco Del Negro; Edward P. Herbst; Ethan Matlin; Reca Sarfati; Frank Schorfheide
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.
Keywords: DSGE models; sequential Monte Carlo; online estimation; forecasting
JEL Codes: C11; C32; C53; E32; E37; E52
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| prior variance (C29) | forecast accuracy (C53) |
| tempering schedule (Y20) | efficiency of posterior estimation (C51) |
| SMC methods (C59) | computational efficiency (C63) |