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Robust Estimates of the New Keynesian Phillips Curve

  • Paul Levine

    (University of Surrey)

  • Luis F. Martins

    (Department of Quantitative Methods, ISCTE, Portugal)

  • Vasco J. Gabriel

    (University of Surrey and NIPE-UM)

In this paper, we examine the hybrid specification of the New Keynesian Phillips Curve (NKPC) proposed by Gali and Gertler (1999) by employing recently developed momentconditions inference procedures. These methods provide a more efficient and reliable econometric framework for the analysis of the NKPC. In particular, we address the issue of parameter identification, providing robust estimates and confidence sets for the model’s parameters. Our results show that the NKPC remains a valid and reliable empirical tool to explain inflation dynamics.

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Paper provided by School of Economics, University of Surrey in its series School of Economics Discussion Papers with number 0206.

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Length: 16 pages
Date of creation: Jan 2006
Date of revision:
Handle: RePEc:sur:surrec:0206
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  1. James H. Stock & Jonathan Wright, 2000. "GMM with Weak Identification," Econometrica, Econometric Society, vol. 68(5), pages 1055-1096, September.
  2. Jordi Galí & Mark Gertler & J. David López-Salido, 2015. "Robustness of the Estimates of the Hybrid New Keynesian Phillips Curve," Working Papers 44, Barcelona Graduate School of Economics.
  3. Whitney K. Newey & Richard J. Smith, 2004. "Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators," Econometrica, Econometric Society, vol. 72(1), pages 219-255, 01.
  4. Newey, Whitney K & West, Kenneth D, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," Review of Economic Studies, Wiley Blackwell, vol. 61(4), pages 631-53, October.
  5. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-58, May.
  6. Yuichi Kitamura & Michael Stutzer, 1997. "An Information-Theoretic Alternative to Generalized Method of Moments Estimation," Econometrica, Econometric Society, vol. 65(4), pages 861-874, July.
  7. Frank Kleibergen, 2005. "Testing Parameters in GMM Without Assuming that They Are Identified," Econometrica, Econometric Society, vol. 73(4), pages 1103-1123, 07.
  8. Jordi Gali & Mark Gertler, 2000. "Inflation Dynamics: A Structural Econometric Analysis," NBER Working Papers 7551, National Bureau of Economic Research, Inc.
  9. Mehmet Caner, 2010. "Exponential Tilting with Weak Instruments: Estimation and Testing," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(3), pages 307-325, 06.
  10. Stanislav Anatolyev, 2005. "GMM, GEL, Serial Correlation, and Asymptotic Bias," Econometrica, Econometric Society, vol. 73(3), pages 983-1002, 05.
  11. Patrik Guggenberger & Richard Smith, 2005. "Generalized empirical likelihood tests in time series models with potential identification failure," CeMMAP working papers CWP01/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  12. Patrik Buggenberger & Richard Smith, 2003. "Generalized empirical likelihood estimators and tests under partial, weak and strong identification," CeMMAP working papers CWP08/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  13. Ma, Adrian, 2002. "GMM estimation of the new Phillips curve," Economics Letters, Elsevier, vol. 76(3), pages 411-417, August.
  14. Marcelo J. Moreira, 2003. "A Conditional Likelihood Ratio Test for Structural Models," Econometrica, Econometric Society, vol. 71(4), pages 1027-1048, 07.
  15. Hansen, Lars Peter & Heaton, John & Yaron, Amir, 1996. "Finite-Sample Properties of Some Alternative GMM Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 262-80, July.
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