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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;

JEL Classification: C60; D84;

https://doi.org/10.20955/wp.2020.027

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Bibliographic Information

Provider: Federal Reserve Bank of St. Louis

Part of Series: Working Papers

Publication Date: 2020-09-19

Number: 2020-027

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