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Identification Using Stability Restrictions
Author(s) -
Magnusson Leandro M.,
Mavroeidis Sophocles
Publication year - 2014
Publication title -
econometrica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 16.7
H-Index - 199
eISSN - 1468-0262
pISSN - 0012-9682
DOI - 10.3982/ecta9612
Subject(s) - inference , generalized method of moments , identification (biology) , moment (physics) , stability (learning theory) , econometrics , economics , new keynesian economics , phillips curve , indirect inference , econometric model , yield (engineering) , mathematical economics , mathematics , computer science , keynesian economics , monetary policy , statistics , artificial intelligence , panel data , physics , machine learning , thermodynamics , botany , classical mechanics , estimator , biology
This paper studies inference in models that are identified by moment restrictions. We show how instability of the moments can be used constructively to improve the identification of structural parameters that are stable over time. A leading example is macroeconomic models that are immune to the well‐known (Lucas (1976)) critique in the face of policy regime shifts. This insight is used to develop novel econometric methods that extend the widely used generalized method of moments (GMM). The proposed methods yield improved inference on the parameters of the new Keynesian Phillips curve.