An auxiliary particle filter for nonlinear dynamic equilibrium models

Yuan Yang and Lu Wang

Economics Letters, 2016, vol. 144, issue C, 112-114

Abstract: 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.

Keywords: DSGE model; Auxiliary particle filter; Bayesian estimation (search for similar items in EconPapers)
JEL-codes: C11 C15 E3 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0165176516301306
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:144:y:2016:i:c:p:112-114

DOI: 10.1016/j.econlet.2016.04.020

Access Statistics for this article

Economics Letters is currently edited by Economics Letters Editorial Office

More articles in Economics Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().