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Title: Sequential Monte Carlo with model tempering
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 two nonlinear dynamic stochastic general equilibrium models. The runtime reductions we obtain range from 27 % to 88 %.  more » « less
Award ID(s):
1851634
NSF-PAR ID:
10420274
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Studies in Nonlinear Dynamics & Econometrics
Volume:
0
Issue:
0
ISSN:
1081-1826
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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