Tempered Particle Filtering

Edward Herbst and Frank Schorfheide

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: The accuracy of particle filters for nonlinear state-space models crucially depends on the proposal distribution that mutates time t − 1 particle values into time t values. In the widely-used bootstrap particle filter this distribution is generated by the state- transition equation. While straightforward to implement, the practical performance is often poor. We develop a self-tuning particle filter in which the proposal distribution is constructed adaptively through a sequence of Monte Carlo steps. Intuitively, we start from a measurement error distribution with an inflated variance, and then gradually reduce the variance to its nominal level in a sequence of steps that we call tempering. We show that the filter generates an unbiased and consistent approximation of the likelihood function. Holding the run time fixed, our filter is substantially more accurate in two DSGE model applications than the bootstrap particle filter.

Keywords: Bayesian Analysis; DSGE Models; Nonlinear Filtering; Monte Carlo Methods (search for similar items in EconPapers)
JEL-codes: C11 C15 E10 (search for similar items in EconPapers)
Pages: 67 pages
Date: 2016-10-25, Revised 2016-10-25
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://economics.sas.upenn.edu/sites/default/file ... /SSRN%2016_017_0.pdf (application/pdf)

Related works:
Journal Article: Tempered particle filtering (2019)
Working Paper: Tempered Particle Filtering (2017)
Working Paper: Tempered Particle Filtering (2016)
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:pen:papers:16-017

Access Statistics for this paper

More papers in PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania 133 South 36th Street, Philadelphia, PA 19104. Contact information at EDIRC.
Bibliographic data for series maintained by Administrator ().