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Maximum Likelihood Estimation of Dynamic Stochastic Theories with an Application to New Keynesian Pricing

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  • André Kurmann

Abstract

This paper proposes a novel Maximum Likelihood (ML) strategy to estimate Euler equations implied by dynamic stochastic theories. The strategy exploits rational expectations cross-equation restrictions, but circumvents the problem of multiple solutions that arises in Sargent's (1979) original work by imposing the restrictions on the forcing variable rather than the endogenous variable of the Euler equation. The paper then contrasts the proposed strategy to an alternative, widely employed method that avoids the multiplicity problem by constraining the ML estimates to yield a unique stable solution. I argue that imposing such a uniqueness condition makes little economic sense and can lead to severe misspecification. To illustrate this point, I estimate Gali and Gertler's (1999) hybrid New Keynesian Phillips Curve using labor income share as the measure of real marginal cost. My ML estimates indicate that forward-looking behavior is predominant and that the model provides a good approximation of U.S. inflation dynamics. By contrast, if the same estimates are constrained to yield a unique stable solution, forward-looking behavior becomes much less important and the model as a whole is rejected.

Suggested Citation

  • André Kurmann, 2004. "Maximum Likelihood Estimation of Dynamic Stochastic Theories with an Application to New Keynesian Pricing," Cahiers de recherche 0421, CIRPEE.
  • Handle: RePEc:lvl:lacicr:0421
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    Citations

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    Cited by:

    1. Sheedy, Kevin D., 2010. "Intrinsic inflation persistence," Journal of Monetary Economics, Elsevier, vol. 57(8), pages 1049-1061, November.
    2. Gali, Jordi & Gertler, Mark & David Lopez-Salido, J., 2005. "Robustness of the estimates of the hybrid New Keynesian Phillips curve," Journal of Monetary Economics, Elsevier, vol. 52(6), pages 1107-1118, September.
    3. Eric JONDEAU & Herve LE BIHAN, 2003. "ML vs GMM Estimates of Hybrid Macroeconomic Models (With an Application to the "New Phillips Curve")," Econometrics 0303006, University Library of Munich, Germany.
    4. Linde, Jesper, 2005. "Estimating New-Keynesian Phillips curves: A full information maximum likelihood approach," Journal of Monetary Economics, Elsevier, vol. 52(6), pages 1135-1149, September.
    5. Bakhshi, Hasan & Khan, Hashmat & Rudolf, Barbara, 2007. "The Phillips curve under state-dependent pricing," Journal of Monetary Economics, Elsevier, vol. 54(8), pages 2321-2345, November.
    6. Luca Fanelli, 2008. "Testing the New Keynesian Phillips Curve Through Vector Autoregressive Models: Results from the Euro Area," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(1), pages 53-66, February.
    7. André Kurmann, 2003. "Quantifying the Uncertainty about the Fit of a New Keynesian Pricing Model: Extended Version," Cahiers de recherche 0344, CIRPEE.
    8. Fanelli, Luca, 2008. "Evaluating New Keynesian Phillips Curve under VAR-Based Learning," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 2, pages 1-24.
    9. Vararat Khemangkorn & Roong Poshyananda Mallikamas & Pranee Sutthasri, 2008. "Inflation Dynamics and Implications on Monetary Policy," Working Papers 2008-02, Monetary Policy Group, Bank of Thailand.

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    More about this item

    Keywords

    Maximum Likelihood; Rational Expectations; New Keynesian Phillips Curve; Inflation; Real Marginal Cost;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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