Identification-Robust Minimum Distance Estimation of the New Keynesian Phillips Curve

Leandro Magnusson and Sophocles Mavroeidis

No 904, Working Papers from Tulane University, Department of Economics

Abstract: Limited-information identification-robust methods on the indexation and price rigidity parameters of the new Keynesian Phillips curve yield very wide confidence intervals. Full-information methods impose more restrictions on the reduced-form dynamics, and thus make more efficient use of the information in the data. We propose identification-robust minimum distance methods for exploiting these additional restrictions and show that they yield considerably smaller confidence intervals for the coefficients of the model compared to their limited-information GMM counterparts. In contrast to previous studies that used GMM, we find evidence of partial but not full indexation, and we obtain sharper inference on the degree of price stickiness.

Keywords: weak identification; minimum distance; GMM; Phillips curve (search for similar items in EconPapers)
JEL-codes: C22 E31 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2009-02
New Economics Papers: this item is included in nep-cba, nep-ecm and nep-mac
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Citations: View citations in EconPapers (1)

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http://repec.tulane.edu/RePEc/pdf/tul0904.pdf First version, 2009 (application/pdf)

Related works:
Journal Article: Identification-Robust Minimum Distance Estimation of the New Keynesian Phillips Curve (2010)
Journal Article: Identification‐Robust Minimum Distance Estimation of the New Keynesian Phillips Curve (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:tul:wpaper:0904

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