DP17035 Sequential Monte Carlo With Model Tempering

Author(s): Marko Mlikota, Frank Schorfheide
Publication Date: February 2022
Keyword(s): Bayesian Computations, Dynamic Stochastic General Equilibrium Models, Sequential Monte Carlo, stochastic volatility, vector autoregressions
JEL(s): C11, C32
Programme Areas: Monetary Economics and Fluctuations
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=17035

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%.