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Empirical Significance of Learning in a New Keynesian Model with Firm-Specific Capital

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  • James Murray

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    (Indiana University Bloomington)

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    Abstract

    This paper examines the empirical significance of learning, a type of adaptive, boundedly rational expectations, in the U.S. economy within the framework of the New Keynesian model. Two popular specifications of the model are estimated: the standard three equation model that does not include capital, and an extended model that allows for endogenous capital accumulation. Estimation results for learning models can be sensitive to the choice for the initial conditions for agents expectations, so four different methods for choosing initial conditions are examined, including jointly estimating the initial conditions with the other parameters of the model. Maximum likelihood results show that learning under all methods for initial conditions lead to very similar predictions as rational expectations, and do not significantly improve the fit the model. The evolution of forecast errors show that the learning models do not out perform the rational expectations model during the run-up of inflation in the 1970s and the subsequent decline in the 1980s, a period of U.S. history which others have suggested learning may play a role. Despite the failure of learning models to better explain the data, analysis of the paths of expectations and structural shocks during the sample show that allowing for learning in the models can lead to different explanations for the data.

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    File URL: http://www.iub.edu/~caepr/RePEc/PDF/2007/CAEPR2007-027.pdf
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    Bibliographic Info

    Paper provided by Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington in its series Caepr Working Papers with number 2007-027.

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    Length: 60 pages
    Date of creation: Jun 2008
    Date of revision:
    Handle: RePEc:inu:caeprp:2007027

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    Related research

    Keywords: Learning; firm-specific capital; New Keynesian model; maximum likelihood;

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    1. Thomas A. Lubik & Frank Schorfheide, 2004. "Testing for Indeterminacy: An Application to U.S. Monetary Policy," American Economic Review, American Economic Association, vol. 94(1), pages 190-217, March.
    2. Roberts, John M, 1995. "New Keynesian Economics and the Phillips Curve," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 27(4), pages 975-84, November.
    3. Ireland, Peter N., 2004. "A method for taking models to the data," Journal of Economic Dynamics and Control, Elsevier, vol. 28(6), pages 1205-1226, March.
    4. Timothy Cogley & Argia M. Sbordone, 2005. "A search for a structural Phillips curve," Staff Reports 203, Federal Reserve Bank of New York.
    5. Giorgio Primiceri, 2005. "Why Inflation Rose and Fell: Policymakers' Beliefs and US Postwar Stabilization Policy," NBER Working Papers 11147, National Bureau of Economic Research, Inc.
    6. James Bullard & Stefano Eusepi, 2003. "Did the Great Inflation Occur Despite Policymaker Commitment to a Taylor Rule?," Computing in Economics and Finance 2003 129, Society for Computational Economics.
    7. Carceles-Poveda, Eva & Giannitsarou, Chryssi, 2007. "Adaptive learning in practice," Journal of Economic Dynamics and Control, Elsevier, vol. 31(8), pages 2659-2697, August.
    8. Jeffrey C. Fuhrer, 2000. "Habit Formation in Consumption and Its Implications for Monetary-Policy Models," American Economic Review, American Economic Association, vol. 90(3), pages 367-390, June.
    9. James B. Bullard & John Duffy, 2004. "Learning and structural change in macroeconomic data," Working Papers 2004-016, Federal Reserve Bank of St. Louis.
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    Cited by:
    1. Evans , George W & Honkapohja, Seppo, 2007. "Expectations, learning and monetary policy: an overview of recent research," Research Discussion Papers 32/2007, Bank of Finland.
    2. James Murray, 2008. "Regime Switching, Learning, and the Great Moderation," Caepr Working Papers 2008-011, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.

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