The New Keynesian Phillips Curve: An Empirical Assessment

Florian Pelgrin, Alain Guay and Richard Luger

No 212, Computing in Economics and Finance 2004 from Society for Computational Economics

Abstract: The recent works of Gali and Gertler (1999) and Gali, Gertler, and Lopez-Salido (2001) provided evidence supporting the NKPC for the United States and the euro area. However, several econometric problems have been discussed in the literature on the empirical relevance of their results (among others, see Rudd and Whelan, 2003; Lindé, 2003; Nason and Smith, 2003). This paper reexamines the empirical relevance of the NKPC for the United States. In particular, we address several important econometric issues related to the estimation and inference procedures typically used for NKPC models. These include the potential bias of GMM estimates in the presence of many instruments, the low power of specification tests based on overidentifying restrictions, and issues related to the estimation of the variance-covariance matrix. In order to mitigate these problems, we estimate various other specifications of the NKPC using the continuously-updated GMM estimator (CUE) developed by Hansen, Heaton and Yaron (1996) and the 3-step GMM estimator of Bonnal and Renault (2001, 2003). These two estimators have better finite-sample properties compared to usual two-step GMM estimator. Our results show that the empirical evidence of the real marginal cost in the NKPC is rather mixed and that the backward-looking component plays a significant role. These results are robust using different sample periods, different measures of inflation and marginal costs.

Keywords: Phillips curve; Inflation dynamics; GMM (search for similar items in EconPapers)
JEL-codes: C13 (search for similar items in EconPapers)
Date: 2004-08-11
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Citations: View citations in EconPapers (14)

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Related works:
Working Paper: The U.S. New Keynesian Phillips Curve: An Empirical Assessment (2004)
Working Paper: The New Keynesian Phillips Curve: An empirical assessment (2004)
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