Global Identification in DSGE Models Allowing for Indeterminacy

Zhongjun Qu and Denis Tkachenko
Authors registered in the RePEc Author Service: Jose L Fillat

No wp2015-001, Boston University - Department of Economics - Working Papers Series from Boston University - Department of Economics

Abstract: This paper presents a framework for analyzing global identification in log linearized DSGE models that encompasses both determinacy and indeterminacy. First, it considers a frequency domain expression for the Kullback-Leibler distance between two DSGE models, and shows that global identification fails if and only if the minimized distance equals zero. This result has three features. (1) It can be applied across DSGE models with different structures. (2) It permits checking whether a subset of frequencies can deliver identification. (3) It delivers parameter values that yield observational equivalence if there is identification failure. Next, the paper proposes a measure for the empirical closeness between two DSGE models for a further understanding of the strength of identification. The measure gauges the feasibility of distinguishing one model from another based on a finite number of observations generated by the two models. It is shown to be equal to the highest possible power in a Gaussian model under a local asymptotic framework. The above theory is illustrated using two small scale and one medium scale DSGE models. The results document that certain parameters can be identified under indeterminacy but not determinacy, that different monetary policy rules can be (nearly) observationally equivalent, and that identification properties can differ substantially between small and medium scale models. For implementation, two procedures are developed and made available, both of which can be used to obtain and thus to cross validate the findings reported in the empirical applications. Although the paper focuses on DSGE models, the results are also applicable to other vector linear processes with well defined spectra, such as the (factor augmented) vector autoregression.

Keywords: Dynamic stochastic general equilibrium models; frequency domain; global identification; multiple equilibria; spectral density (search for similar items in EconPapers)
JEL-codes: C10 C30 C52 E1 E3 (search for similar items in EconPapers)
Date: 2015-08
New Economics Papers: this item is included in nep-int
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Global Identification in DSGE Models Allowing for Indeterminacy (2017)
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