REAL-TIME, ADAPTIVE LEARNING VIA PARAMETERIZED EXPECTATIONS

Michele Berardi and John Duffy

Macroeconomic Dynamics, 2015, vol. 19, issue 2, 245-269

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.

Date: 2015
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Working Paper: Real-Time, Adaptive Learning via Parameterized Expectations (2010)
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