Sequential Monte Carlo With Model Tempering

• Tác giả: Marko Mlikota, Frank Schorfheide
• Ngôn ngữ: eng
• Ký hiệu phân loại: 539.54 Modern physics
• Thông tin xuất bản: 2022
• Bộ sưu tập: Metadata
• ID: 194411

Tóm tắt

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%.