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 University of Pittsburgh, Department of Economics in its series Working Papers with number 400.
Date of creation: Jul 2010
Date of revision: Aug 2010
Other versions of this item:
- Michele Berardi & John Duffy, 2010. "Real-Time, Adaptive Learning via Parameterized Expectations," Centre for Growth and Business Cycle Research Discussion Paper Series 147, Economics, The Univeristy of Manchester.
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-08-14 (All new papers)
- NEP-CBA-2010-08-14 (Central Banking)
- NEP-ORE-2010-08-14 (Operations Research)
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