Identification of DSGE models—The effect of higher-order approximation and pruning
Willi Mutschler ()
Journal of Economic Dynamics and Control, 2015, vol. 56, issue C, 34-54
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 (search for similar items in EconPapers)
JEL-codes: C10 C51 C52 E1 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
Working Paper: Identification of DSGE Models - the Effect of Higher-Order Approximation and Pruning (2014)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:56:y:2015:i:c:p:34-54
Access Statistics for this article
Journal of Economic Dynamics and Control is currently edited by J. Bullard, C. Chiarella, H. Dawid, C. H. Hommes, P. Klein and C. Otrok
More articles in Journal of Economic Dynamics and Control from Elsevier
Bibliographic data for series maintained by Haili He ().