Sequential Monte Carlo With Model Tempering

Marko Mlikota and Frank Schorfheide

No 17035, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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 (search for similar items in EconPapers)
JEL-codes: C11 C32 (search for similar items in EconPapers)
Date: 2022-02
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Working Paper: Sequential Monte Carlo With Model Tempering (2022)
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