Non-linear DSGE Models and The Central Difference Kalman Filter

Martin Andreasen

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: This paper introduces a Quasi Maximum Likelihood (QML) approach based on the Central Difference Kalman Filter (CDKF) to estimate non-linear DSGE models with potentially non-Gaussian shocks. We argue that this estimator can be expected to be consistent and asymptotically normal for DSGE models solved up to third order. A Monte Carlo study shows that this QML estimator is basically unbiased and normally distributed infi?nite samples for DSGE models solved using a second order or a third order approximation. These results hold even when structural shocks are Gaussian, Laplace distributed, or display stochastic volatility.

Keywords: Non-linear filtering; Non-Gaussian shocks; Quasi Maximum Likelihood; Stochastic volatility; Third order perturbation. (search for similar items in EconPapers)
JEL-codes: C13 C15 E10 E32 (search for similar items in EconPapers)
Pages: 47
Date: 2010-07-20
New Economics Papers: this item is included in nep-cba, nep-dge, nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://repec.econ.au.dk/repec/creates/rp/10/rp10_30.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:aah:create:2010-30

Access Statistics for this paper

More papers in CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
Bibliographic data for series maintained by ().