Real-Time, Adaptive Learning via Parameterized Expectations

Michele Berardi and John Duffy

Centre for Growth and Business Cycle Research Discussion Paper Series from Economics, The University of Manchester

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.

Pages: 20 pages
Date: 2010
New Economics Papers: this item is included in nep-cba
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Journal Article: REAL-TIME, ADAPTIVE LEARNING VIA PARAMETERIZED EXPECTATIONS (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:man:cgbcrp:147

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