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Identification of DSGE Models - the Effect of Higher-Order Approximation and Pruning

Willi Mutschler ()

No 3314, CQE Working Papers from Center for Quantitative Economics (CQE), University of Muenster

Abstract: Several formal methods have been proposed to check local identification in linearized DSGE models using rank criteria. Recently there has been huge progress in the estimation of non-linear DSGE models, yet formal identification criteria are missing. The contribution of the paper is threefold: First, we extend the existent methods to higher-order approximations and establish rank criteria for local identification given the pruned state-space representation. It is shown that this may improve overall identification of a DSGE model via imposing additional restrictions on the moments and spectrum. Second, we derive analytical derivatives of the reduced-form matrices, unconditional moments and spectral density for the pruned state-space system. Third, using a second-order approximation, we are able to identify previously non-identifiable parameters: namely the parameters governing the investment adjustment costs in the Kim (2003) model and all parameters in the An and Schorfheide (2007) model, including the coeffcients of the Taylor-rule.

Keywords: non-linear DSGE; rank condition; analytical derivatives; pruned state-space (search for similar items in EconPapers)
JEL-codes: C10 C51 C52 E1 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2014-10
New Economics Papers: this item is included in nep-dge, nep-ecm and nep-ore
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Journal Article: Identification of DSGE models—The effect of higher-order approximation and pruning (2015) Downloads
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