Real-Time, Adaptive Learning via Parameterized Expectations
AbstractWe 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.
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Bibliographic InfoPaper provided by Economics, The Univeristy of Manchester in its series Centre for Growth and Business Cycle Research Discussion Paper Series with number 147.
Length: 20 pages
Date of creation: 2010
Date of revision:
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Other versions of this item:
- John Duffy, 2010. "Real-Time, Adaptive Learning via Parameterized Expectations," Working Papers 400, University of Pittsburgh, Department of Economics, revised Aug 2010.
- 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, and Information
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