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REAL-TIME, ADAPTIVE LEARNING VIA PARAMETERIZED EXPECTATIONS

Published online by Cambridge University Press:  09 August 2013

Michele Berardi  and
John Duffy
Michele Berardi*
Affiliation:
University of Manchester
John Duffy
Affiliation:
University of Pittsburgh
*
Address correspondence to: Michele Berardi, Economics, School of Social Sciences, Arthur Lewis Building, Oxford Road, University of Manchester, Manchester M13 9PL, UK; e-mail: michele.berardi@manchester.ac.uk.
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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.

Keywords

Rational ExpectationsLearningParameterized ExpectationsNumerical Methods
Type
Articles
Information
Macroeconomic Dynamics , Volume 19 , Issue 2 , March 2015 , pp. 245 - 269
Copyright
Copyright © Cambridge University Press 2013 

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