Identification of prior information via moment-matching

Stephen Sacht

No 2014-04, Economics Working Papers from Christian-Albrechts-University of Kiel, Department of Economics

Abstract: In this paper we apply a sensitivity analysis regarding two types of prior information considered within the Bayesian estimation of a standard hybrid New-Keynesian model. In particular, we shed a light on the impact of micro- and macropriors on the estimation outcome. First, we investigate the impact of the transformation of those model parameters which are bounded to the unit interval, in order to allow for a more diffuse prior distribution. Second, we combine the Moment-Matching (MM, Franke et al. (2012)) and Bayesian technique in order to evaluate macropriors. In this respect we define a two-stage estimation procedure - the so-called Moment-Matching based Bayesian (MoMBay) estimation approach - where we take the point estimates evaluated via MM and consider them as prior mean values of the parameters within Bayesian estimation. We show that while (transformed) micropriors are often used in the literature, applying macropriors evaluated via the MoMBay approach leads to a better fit of the structural model to the data. Furthermore, there is evidence for intrinsic (degree of price indexation) rather than extrinsic (autocorrelation in the shock process) persistence - an observation which stands in contradiction to the results documented in the recent literature.

Keywords: Bayesian estimation; moment-matching estimation; mombay estimation; New-Keynesian model; micropriors; macropriors (search for similar items in EconPapers)
JEL-codes: C11 C32 C52 E3 (search for similar items in EconPapers)
Date: 2014
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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