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Testing the Invariance of Expectations Models of Inflation

Author

Listed:
  • Nymoen, Ragnar

    () (Dept. of Economics, University of Oslo)

  • L. Castle, Jennifer

    (Magdalen college, Oxford)

  • A. Doornik, Jurgen

    (Nuffield College, Oxford)

  • F. Hendry, David

    (University of Oxford)

Abstract

The new-Keynesian Phillips curve (NKPC) includes expected future inflation to explain current inflation. Such models are estimated by replacing the expected value by the future outcome, using InstrumentalVariables or Generalized Method of Momentsmethods. However, the underlying theory does not allow for various non-stationarities–although crises, breaks and regimes shifts are relatively common. We analytically investigate the consequences for NKPC estimation of breaks in data processes, then apply the new technique of impulse-indicator saturation to salient published studies to check their viability. The coefficient of the future value becomes insignificant after modelling breaks.

Suggested Citation

  • Nymoen, Ragnar & L. Castle, Jennifer & A. Doornik, Jurgen & F. Hendry, David, 2010. "Testing the Invariance of Expectations Models of Inflation," Memorandum 21/2010, Oslo University, Department of Economics.
  • Handle: RePEc:hhs:osloec:2010_021
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    File URL: https://www.sv.uio.no/econ/english/research/unpublished-works/working-papers/pdf-files/2010/Memo-21-2010.pdf
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    Cited by:

    1. Russell, Bill & Chowdhury, Rosen Azad, 2013. "Estimating United States Phillips curves with expectations consistent with the statistical process of inflation," Journal of Macroeconomics, Elsevier, vol. 35(C), pages 24-38.
    2. Abbas, Syed K. & Bhattacharya, Prasad Sankar & Sgro, Pasquale, 2016. "The new Keynesian Phillips curve: An update on recent empirical advances," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 378-403.
    3. Cornea, A. & Hommes, C.H. & Massaro, D., 2012. "Behavioral Heterogeneity in U.S. Inflation Dynamics," CeNDEF Working Papers 12-03, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    4. Syed Kanwar Abbas & Prasad Sankar Bhattacharya & Debdulal Mallick & Pasquale Sgro, 2016. "The New Keynesian Phillips Curve in a Small Open Economy: Empirical Evidence from Australia," The Economic Record, The Economic Society of Australia, vol. 92(298), pages 409-434, September.
    5. De Grauwe, Paul & Macchiarelli, Corrado, 2015. "Animal spirits and credit cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 59(C), pages 95-117.
    6. Sophocles Mavroeidis & Mikkel Plagborg-Møller & James H. Stock, 2014. "Empirical Evidence on Inflation Expectations in the New Keynesian Phillips Curve," Journal of Economic Literature, American Economic Association, vol. 52(1), pages 124-188, March.
    7. Hendry, David F., 2011. "On adding over-identifying instrumental variables to simultaneous equations," Economics Letters, Elsevier, vol. 111(1), pages 68-70, April.
    8. Nymoen, Ragnar & Swensen, Anders Rygh & Tveter, Eivind, 2012. "Interpreting the evidence for New Keynesian models of inflation dynamics," Journal of Macroeconomics, Elsevier, vol. 34(2), pages 253-263.
    9. repec:taf:emetrv:v:35:y:2016:i:7:p:1251-1270 is not listed on IDEAS
    10. Mariano Kulish & Adrian Pagan, 2016. "Issues in Estimating New Keynesian Phillips Curves in the Presence of Unknown Structural Change," Econometric Reviews, Taylor & Francis Journals, vol. 35(7), pages 1251-1270, August.
    11. J. James Reade & Ulrich Volz, 2011. "From the General to the Specific," Discussion Papers 11-18, Department of Economics, University of Birmingham.

    More about this item

    Keywords

    New-Keynesian Phillips curve; Inflation expectations; Structural breaks; Impulse-indicator saturation.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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