Working Paper Revision
Convergence to Rational Expectations in Learning Models: A Note of Caution
Abstract: This paper illustrates a challenge in analyzing the learning algorithms resulting in second-order difference equations. We show in a simple monetary model that the learning dynamics do not converge to the rational expectations monetary steady state. We then show that to guarantee convergence, the gain parameter used in the learning rule has to be restricted based on economic fundamentals in the monetary model.
Keywords: rational expectations equilibrium; learning algorithm; convergence; gain function;
https://doi.org/10.20955/wp.2020.027
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Provider: Federal Reserve Bank of St. Louis
Part of Series: Working Papers
Publication Date: 2020-09-19
Number: 2020-027
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- Working Paper Revision: Convergence to Rational Expectations in Learning Models: A Note of Caution