Efficient Likelihood Evaluation of State-Space Representations

David N. DeJong, Hariharan Dharmarajan, Roman Liesenfeld, Guilherme Moura () and Jean-Francois Richard

Working Papers from Czech National Bank, Research and Statistics Department

Abstract: We develop a numerical procedure that facilitates efficient likelihood evaluation in applications involving non-linear and non-Gaussian state-space models. The procedure employs continuous approximations of filtering densities, and delivers unconditionally optimal global approximations of targeted integrands to achieve likelihood approximation. Optimized approximations of targeted integrands are constructed via efficient importance sampling. Resulting likelihood approximations are continuous functions of model parameters, greatly enhancing parameter estimation. We illustrate our procedure in applications to dynamic stochastic general equilibrium models.

Keywords: Adaption; dynamic stochastic general equilibrium model; efficient importance sampling; kernel density approximation; particle filter. (search for similar items in EconPapers)
JEL-codes: C63 C68 (search for similar items in EconPapers)
Date: 2009-12
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.cnb.cz/export/sites/cnb/en/economic-re ... wp/cnbwp_2009_15.pdf

Related works:
Journal Article: Efficient Likelihood Evaluation of State-Space Representations (2013)
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:cnb:wpaper:2009/15

Access Statistics for this paper

More papers in Working Papers from Czech National Bank, Research and Statistics Department Contact information at EDIRC.
Bibliographic data for series maintained by Tomas Karhanek ().