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Evaluating the New Keynesian Phillips Curve under VAR-Based Learning

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  • Fanelli, Luca

Abstract

This paper proposes the econometric evaluation of the New Keynesian Phillips Curve (NKPC) in the euro area, under a particular specification of the adaptive learning hypothesis. The key assumption is that agents? perceived law of motion is a Vector Autoregressive (VAR) model, whose coefficients are updated by maximum likelihood estimation, as the information set increases over time. Each time new data is available, likelihood ratio tests for the crossequation restrictions that the NKPC imposes on the VAR are computed and compared with a proper set of critical values which take the sequential nature of the test into account. The analysis is developed by focusing on the case where the variables entering the NKPC can be approximated as nonstationary cointegrated processes, assuming that the agents? recursive estimation algorithm involves only the parameters associated with the short run transient dynamics of the system. Results on quarterly data relative to the period 1981?2006 show that: (i) the euro area inflation rate and the wage share are cointegrated; (ii) the cointegrated version of the ?hybrid? NKPC is sharply rejected under the rational expectations hypothesis; (iii) the model is supported by the data over relevant fractions of the chosen monitoring period, 1986?2006, under the adaptive learning hypothesis, although this evidence does not appear compelling.

Suggested Citation

  • Fanelli, Luca, 2008. "Evaluating the New Keynesian Phillips Curve under VAR-Based Learning," Economics Discussion Papers 2008-15, Kiel Institute for the World Economy (IfW).
  • Handle: RePEc:zbw:ifwedp:7257
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    Cited by:

    1. Giovanni Angelini & Luca Fanelli, 2016. "Misspecification and Expectations Correction in New Keynesian DSGE Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(5), pages 623-649, October.
    2. Katarina Juselius, 2011. "On the Role of Theory and Evidence in Macroeconomics," Chapters, in: John B. Davis & D. Wade Hands (ed.), The Elgar Companion to Recent Economic Methodology, chapter 17, Edward Elgar Publishing.
    3. Luca Fanelli & Giulio Palomba, 2011. "Simulation‐based tests of forward‐looking models under VAR learning dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 762-782, August.
    4. Frédérique Bec & Patrick Kanda, 2019. "Is inflation driven by survey-based, VAR-based or myopic expectations?," Working Papers hal-02175836, HAL.
    5. Katarina Juselius, 2009. "Time to reject the privileging of economic theory over empirical evidence? A Reply to Lawson (2009)," Discussion Papers 09-16, University of Copenhagen. Department of Economics.
    6. Bec, Frédérique & Kanda, Patrick, 2020. "Is inflation driven by survey-based, VAR-based or myopic expectations? An empirical assessment from US real-time data," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    7. Ordóñez, Javier & Jusélius, Katarina, 2009. "Balassa-Samuelson and Wage, Price and Unemployment Dynamics in the Spanish Transition to EMU Membership," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 3, pages 1-30.
    8. Jusélius, Katarina, 2009. "Special Issue on Using Econometrics for Assessing Economic Models: An Introduction," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 3, pages 1-20.

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    More about this item

    Keywords

    Adaptive learning; cointegration; cross-equation restrictions; forward-looking model of inflation dynamics; New Keynesian Phillips Curve;
    All these keywords.

    JEL classification:

    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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