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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Veröffentlicht in:Economics letters Jg. 144; S. 112 - 114
Hauptverfasser: Yang, Yuan, Wang, Lu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.07.2016
Elsevier Science Ltd
Schlagworte:
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-Zugang:Volltext
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Beschreibung
Zusammenfassung: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 particles to achieve comparable results as currently available particle filters. We illustrate our algorithm in Bayesian estimation of a new Keynesian macroeconomic model. •An auxiliary particle filter for nonlinear DSGE models is developed.•It achieves more accurate likelihood approximation and accelerates the convergence of posterior sampler.•It requires much fewer particles than generally suggested to obtain comparable efficiency.
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ISSN:0165-1765
1873-7374
DOI:10.1016/j.econlet.2016.04.020