Identification of DSGE models—The effect of higher-order approximation and pruning

Mutschler W


Abstract
This paper shows how to check rank criteria for a local identification of nonlinear DSGE models, given higher-order approximations and pruning. This approach imposes additional restrictions on (higher-order) moments and polyspectra, which can be used to identify parameters that are unidentified in a first-order approximation. The identification procedures are demonstrated by means of the Kim (2003) and the An and Schorfheide (2007) models. Both models are identifiable with a second-order approximation. Furthermore, analytical derivatives of unconditional moments, cumulants and corresponding polyspectra up to fourth order are derived for the pruned state-space.

Keywords
Identification; Pruning; Higher-order moments; Cumulants; Polyspectra; Analytical derivatives



Publication type
Research article (journal)

Peer reviewed
Yes

Publication status
Published

Year
2015

Journal
Journal of Economic Dynamics and Control

Volume
56

Start page
34

End page
54

Language
English

ISSN
0165-1889

DOI

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