Working Paper: CEPR ID: DP17035
Authors: Marko Mlikota; Frank Schorfheide
Abstract: Modern macroeconometrics often relies on time series models for which it is time-consuming to evaluate the likelihood function. We demonstrate how Bayesian computations for such models can be drastically accelerated by reweighting and mutating posterior draws from an approximating model that allows for fast likelihood evaluations, into posterior draws from the model of interest, using a sequential Monte Carlo (SMC) algorithm. We apply the technique to the estimation of a vector autoregression with stochastic volatility and a nonlinear dynamic stochastic general equilibrium model. The runtime reductions we obtain range from 27% to 88%.
Keywords: Bayesian computations; Dynamic stochastic general equilibrium models; Sequential Monte Carlo; Stochastic volatility; Vector autoregressions
JEL Codes: C11; C32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| approximating model (m0) (C51) | target model (m1) (C52) |
| model tempering (C59) | computational efficiency (C63) |
| model tempering (C59) | runtime reduction in vector autoregression (C32) |
| model tempering (C59) | runtime reduction in DSGE model (E13) |