A Note on Particle Filters Applied to DSGE Models

Angelo Fasolo

No 281, Working Papers Series from Central Bank of Brazil, Research Department

Abstract: This paper compares the properties of two particle filters – the Bootstrap Filter and the Auxiliary Particle Filter – applied to the computation of the likelihood of artificial data simulated from a basic DSGE model with nominal and real rigidities. Particle filters are compared in terms of speed, quality of the approximation of the probability density function of data and tracking of state variables. Results show that there is a case for the use of the Auxiliary Particle Filter only when the researcher uses a large number of observable variables and the number of particles used to characterize the likelihood is relatively low. Simulations also show that the largest gains in tracking state variables in the model are found when the number of particles is between 20,000 and 30,000, suggesting a boundary for this number.

Date: 2012-06
New Economics Papers: this item is included in nep-cmp, nep-dge and nep-ecm
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.bcb.gov.br/content/publicacoes/WorkingPaperSeries/wps281.pdf (application/pdf)

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:bcb:wpaper:281

Access Statistics for this paper

More papers in Working Papers Series from Central Bank of Brazil, Research Department
Bibliographic data for series maintained by Rodrigo Barbone Gonzalez ().