Jump to ContentJump to Main Navigation
Bayesian Estimation of DSGE Models$
Users without a subscription are not able to see the full content.

Edward P. Herbst and Frank Schorfheide

Print publication date: 2015

Print ISBN-13: 9780691161082

Published to Princeton Scholarship Online: October 2017

DOI: 10.23943/princeton/9780691161082.001.0001

Show Summary Details
Page of

PRINTED FROM PRINCETON SCHOLARSHIP ONLINE (www.princeton.universitypressscholarship.com). (c) Copyright Princeton University Press, 2018. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a monograph in HSO for personal use (for details see www.princeton.universitypressscholarship.com/page/privacy-policy).date: 15 October 2018

Sequential Monte Carlo Methods

Sequential Monte Carlo Methods

Chapter:
(p.100) Chapter 5 Sequential Monte Carlo Methods
Source:
Bayesian Estimation of DSGE Models
Author(s):

Edward P. Herbst

Frank Schorfheide

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691161082.003.0005

This chapter analyzes Sequential Monte Carlo (SMC) algorithms and how they were initially developed to solve filtering problems that arise in nonlinear state–space models. The first paper that applied SMC techniques to posterior inference in DSGE models is Creal (2007). Herbst and Schorfheide (2014) developed the algorithm further, provided some convergence results for an adaptive version of the algorithm, and showed that a properly tailored SMC algorithm delivers more reliable posterior inference for largescale DSGE models with multimodal posteriors than the widely used RMWHV algorithm. An additional advantage of the SMC algorithms over MCMC algorithms, on the computational front, highlighted by Durham and Geweke (2014), is that SMC is much more amenable to parallelization.

Keywords:   SMC algorithms, state–space models, posterior inference, DSGE models, multimodal posteriors, RMWHV algorithm, MCMC algorithms

Princeton Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs , and if you can't find the answer there, please contact us.