Assessing Identifying Restrictions in SVAR Models

Michele Piffer

No 1563, Discussion Papers of DIW Berlin from DIW Berlin, German Institute for Economic Research

Abstract: This paper proposes a Bayesian approach to assess if the data support candidate set-identifying restrictions for Vector Autoregressive models. The researcher is uncertain about the validity of some sign restrictions that she is contemplating to use. She therefore expresses her uncertainty with a prior distribution that covers the parameter space both where the restrictions are satisfied and where they are not satisfied. I show that the data determine whether the probability mass in favour of the restrictions increases or not from prior to posterior. Using two applications, I find support for the restrictions used by Baumeister & Hamilton (2015a) in their two-equation model of labor demand and supply, and I find support for the true data generating process in a simulation exercise on the New Keynesian model.

Keywords: Identification; Bayesian econometrics; sign restrictions (search for similar items in EconPapers)
JEL-codes: C11 C32 (search for similar items in EconPapers)
Pages: 41 p.
Date: 2016
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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