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Identification and Generalized Band Spectrum Estimation of the New Keynesian Phillips Curve

Junjie Guo (), Juan Carlos Escanciano () and Jinho Choi ()
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Junjie Guo: Indiana University

No 2017-014, CAEPR Working Papers from Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington

Abstract: This article proposes a new identification strategy and a new estimation method for the hybrid New Keynesian Phillips curve (NKPC). Unlike the predominant Generalized Method of Moments (GMM) approach, which leads to weak identification of the NKPC with U.S. postwar data, our nonparametric method exploits nonlinear variation in inflation dynamics and provides supporting evidence of point-identification. This article shows that identification of the NKPC is characterized by two conditional moment restrictions. This insight leads to a quantitative method to assess identification in the NKPC. For estimation, the article proposes a closed-form Generalized Band Spectrum Estimator (GBSE) that effectively uses information from the conditional moments, accounts for nonlinear variation, and permits a focus on short-run dynamics. Applying the GBSE to U.S postwar data, we find a significant coefficient of marginal cost and that the forward-looking component and the inflation inertia are both equally quantitatively important in explaining the short-run inflation dynamics, substantially reducing sampling uncertainty relative to existing GMM estimates.

Keywords: Point-identification; New Keynesian Phillips curve; Weak instruments; Nonlinear dependence; Generalized spectrum (search for similar items in EconPapers)
JEL-codes: C26 C51 E31 E37 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2017-11
New Economics Papers: this item is included in nep-ecm and nep-mac
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