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Are New Keynesian Phillips Curves Identified ?

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  • Maral Kichian
  • Jean-Marie Dufour
  • Lynda Khalaf

Abstract

In this paper we use optimal-instrument and new finite-sample methods to test the empirical relevance of the New Keynesian Phillips curve (NKPC) equation. Unlike generalized method of moments-based methods, these generalized Anderson-Rubin tests are immune to the presence of weak instruments, and allow, by construction, to assess the identification status of a model. Our results are illustrated using the Gali-Gertler (1999) NKPC specifications and data, as well as a survey-based inflation expectation series from the Philadelphia Fed. Our test rejects the reported Gali-Gertler estimates, conditional on their choice of instruments. Nevertheless, and in contrast to Ma (2002), we do obtain relatively informative confidence sets. This provides support for NKPC equations and illustrates the usefulness of using exact procedures and optimal instruments in IV-based estimations. In particular, our results reveal that firms fix prices in a predominantly backward-looking manner, but that they adjust prices every quarter or so. Furthermore, the outcomes indicate that it is difficult to pin-point the extent of the importance of marginal costs for the inflation process.

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Bibliographic Info

Paper provided by Society for Economic Dynamics in its series 2004 Meeting Papers with number 601.

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Date of creation: 2004
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Handle: RePEc:red:sed004:601

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Keywords: NKPC; optimal instruments; Anderson-Rubin test;

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References

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  1. Gali, Jordi & Gertler, Mark & Lopez-Salido, J. David, 2001. "European inflation dynamics," European Economic Review, Elsevier, vol. 45(7), pages 1237-1270.
  2. Kleibergen, F.R. & Zivot, E., 1998. "Bayesian and classical approaches to instrumental variable regression," Econometric Institute Research Papers EI 9835, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  3. Douglas Staiger & James H. Stock, 1994. "Instrumental Variables Regression with Weak Instruments," NBER Technical Working Papers 0151, National Bureau of Economic Research, Inc.
  4. Jiahui Wang & Eric Zivot, 1998. "Inference on Structural Parameters in Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 66(6), pages 1389-1404, November.
  5. Nelson, C.R. & Startz, R. & Zivot, E., 1996. "Valid Confidence Intervals and Inference in the Presence of Weak Instruments," Working Papers 96-15, University of Washington, Department of Economics.
  6. Gali, Jordi & Gertler, Mark, 1999. "Inflation dynamics: A structural econometric analysis," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 195-222, October.
  7. Ma, Adrian, 2002. "GMM estimation of the new Phillips curve," Economics Letters, Elsevier, vol. 76(3), pages 411-417, August.
  8. Jean-Marie Dufour, 2003. "Identification, Weak Instruments and Statistical Inference in Econometrics," CIRANO Working Papers 2003s-49, CIRANO.
  9. Jeffrey C. Fuhrer, 1995. "The [un]importance of forward-looking behavior in price specifications," Working Papers 95-6, Federal Reserve Bank of Boston.
  10. Fuhrer, Jeff & Moore, George, 1995. "Inflation Persistence," The Quarterly Journal of Economics, MIT Press, vol. 110(1), pages 127-59, February.
  11. Dufour, Jean-Marie & Jasiak, Joann, 2001. "Finite Sample Limited Information Inference Methods for Structural Equations and Models with Generated Regressors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 42(3), pages 815-43, August.
  12. Frank Kleibergen, 2002. "Pivotal Statistics for Testing Structural Parameters in Instrumental Variables Regression," Econometrica, Econometric Society, vol. 70(5), pages 1781-1803, September.
  13. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-29, October.
  14. Dufour, J.M. & Khalaf, L., 2000. "Simulation-Based Finite and Large Sample Tests in Multivariate Regressions," Cahiers de recherche 2000-10, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  15. DUFOUR, Jean-Marie & TAAMOUTI, Mohamed, 2003. "Projection-Based Statistical Inference in Linear Structural Models with Possibly Weak Instruments," Cahiers de recherche 08-2003, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  16. James H. Stock & Jonathan Wright, 2000. "GMM with Weak Identification," Econometrica, Econometric Society, vol. 68(5), pages 1055-1096, September.
  17. DUFOUR, Jean-Marie, 2003. "Identification, Weak Instruments and Statistical Inference in Econometrics," Cahiers de recherche 10-2003, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  18. Lynda Khalaf & Jean-Marie Dufour, 2004. "Simulation-Based Finite-Sample Inference in Simultaneous Equations," Econometric Society 2004 North American Summer Meetings 239, Econometric Society.
  19. Marcelo J. Moreira, 2003. "A General Theory of Hypothesis Testing in the Simultaneous Equations Model," Harvard Institute of Economic Research Working Papers 1992, Harvard - Institute of Economic Research.
  20. Jean-Marie Dufour, 1997. "Some Impossibility Theorems in Econometrics with Applications to Structural and Dynamic Models," Econometrica, Econometric Society, vol. 65(6), pages 1365-1388, November.
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Cited by:
  1. Sophocles Mavroeidis, 2006. "Testing the New Keynesian Phillips Curve Without Assuming Identification," Working Papers 2006-13, Brown University, Department of Economics.
  2. Mazumder, Sandeep, 2010. "The new Keynesian Phillips curve and the cyclicality of marginal cost," Journal of Macroeconomics, Elsevier, vol. 32(3), pages 747-765, September.

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