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Real-Time, Adaptive Learning Via Parameterized Expectations

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  • Berardi, Michele
  • Duffy, John

Abstract

We explore real-time adaptive nonlinear learning dynamics in stochastic macroeconomic systems. Rather than linearizing nonlinear Euler equations where expectations play a role around a steady state, we instead approximate the nonlinear expected values using the method of parameterized expectations. Further, we assume that these approximated expectations are updated in real time as new data become available. We argue that this method of real-time parameterized expectations learning provides a plausible alternative to real-time adaptive learning dynamics under linearized versions of the same nonlinear system, and we provide a comparison of the two approaches.

Suggested Citation

  • Berardi, Michele & Duffy, John, 2015. "Real-Time, Adaptive Learning Via Parameterized Expectations," Macroeconomic Dynamics, Cambridge University Press, vol. 19(2), pages 245-269, March.
    • Handle: RePEc:cup:macdyn:v:19:y:2015:i:02:p:245-269_00
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    References listed on IDEAS

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    1. Evans, George W & Honkapohja, Seppo, 1995. "Local Convergence of Recursive Learning to Steady States and Cycles in Stochastic Nonlinear Models," Econometrica, Econometric Society, vol. 63(1), pages 195-206, January.
    2. George W. Evans & Seppo Honkapohja, 2006. "Monetary Policy, Expectations and Commitment," Scandinavian Journal of Economics, Wiley Blackwell, vol. 108(1), pages 15-38, March.
    3. den Haan, Wouter J & Marcet, Albert, 1990. "Solving the Stochastic Growth Model by Parameterizing Expectations," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 31-34, January.
    4. Bruce Preston, 2005. "Learning about Monetary Policy Rules when Long-Horizon Expectations Matter," International Journal of Central Banking, International Journal of Central Banking, vol. 1(2), September.
    5. Albert Marcet & David A. Marshall, 1994. "Solving nonlinear rational expectations models by parameterized expectations: convergence to stationary solutions," Discussion Paper / Institute for Empirical Macroeconomics 91, Federal Reserve Bank of Minneapolis.
    6. Eusepi, Stefano, 2007. "Learnability and monetary policy: A global perspective," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1115-1131, May.
    7. Chen, Xiaohong & White, Halbert, 1998. "Nonparametric Adaptive Learning with Feedback," Journal of Economic Theory, Elsevier, vol. 82(1), pages 190-222, September.
    8. George W. Evans & Seppo Honkapohja, 2010. "Corrigendum: Monetary Policy, Expectations and Commitment," Scandinavian Journal of Economics, Wiley Blackwell, vol. 112(3), pages 640-641, September.
    9. Taylor, John B & Uhlig, Harald, 1990. "Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 1-17, January.
    10. Marimon, Ramon & Scott, Andrew (ed.), 1999. "Computational Methods for the Study of Dynamic Economies," OUP Catalogue, Oxford University Press, number 9780198294979, Decembrie.
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    Cited by:

    1. Yutaka Kurihara, 2017. "Recent monetary policy effects on Japanese macroeconomy," Journal of Economic and Financial Studies (JEFS), LAR Center Press, vol. 5(5), pages 12-17, October.
    2. Yutaka Kurihara, 2016. "Can the Disparity between GDP and GDP Forecast Cause Economic Instability? The Recent Japanese Case," International Journal of Economics and Financial Research, Academic Research Publishing Group, vol. 2(8), pages 155-160, 08-2016.
    3. Berardi Michele, 2012. "Heterogeneous Learning Dynamics and Speed of Convergence," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(4), pages 1-20, October.
    4. Brecht Boone & Ewoud Quaghebeur, 2017. "Real-Time Parameterized Expectations And The Effects Of Government Spending," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 17/939, Ghent University, Faculty of Economics and Business Administration.
    5. Casares, Miguel & Vázquez, Jesús, 2016. "Data Revisions In The Estimation Of Dsge Models," Macroeconomic Dynamics, Cambridge University Press, vol. 20(7), pages 1683-1716, October.

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    More about this item

    JEL classification:

    • C62 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Existence and Stability Conditions of Equilibrium
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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