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Real-Time, Adaptive Learning via Parameterized Expectations

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  • John Duffy

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 suppose that these approximated expectations are updated in real time as new data become available. We explore whether 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.

Suggested Citation

  • John Duffy, 2010. "Real-Time, Adaptive Learning via Parameterized Expectations," Working Paper 400, Department of Economics, University of Pittsburgh, revised Aug 2010.
    • Handle: RePEc:pit:wpaper:400
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    File URL: http://www.econ.pitt.edu/papers/John_bd0710.pdf
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    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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