An auxiliary particle filter for nonlinear dynamic equilibrium models

We develop a particle filter algorithm to approximate the likelihood function of nonlinear dynamic stochastic general equilibrium models. The new algorithm reduces the Monte Carlo variance of likelihood approximation and accelerates the convergence of posterior sampler. It requires much fewer partic...

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Bibliographic Details
Published in:	Economics letters Vol. 144; pp. 112 - 114
Main Authors:	Yang, Yuan, Wang, Lu
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.07.2016 Elsevier Science Ltd
Subjects:	Algorithms Approximation Auxiliary particle filter Bayesian analysis Bayesian estimation Bayesian theory Convergence DSGE model Equilibrium filters Function words Keynesian theory Macroeconomics Monte Carlo simulation Nonlinear programming probability Stochastic models Studies variance Auxiliary particle filter C11 Bayesian estimation DSGE model C15 E3
ISSN:	0165-1765, 1873-7374
Online Access:	Get full text
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