Convergence to Rational Expectations in Learning Models: A Note of Caution

YiLi Chien, Inkoo Cho and B Ravikumar

No 2020-027, Working Papers from Federal Reserve Bank of St. Louis

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 (search for similar items in EconPapers)
JEL-codes: C60 D84 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2020-08-29, Revised 2020-09-19
New Economics Papers: this item is included in nep-ore and nep-upt
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Journal Article: Convergence to Rational Expectations in Learning Models: A Note of Caution (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedlwp:88664

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DOI: 10.20955/wp.2020.027

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