Social learning and monetary policy rules
By: by Jasmina Arifovic, James B. Bullard, and Olena Kostyshyna.
Contributors: Bullard, James.; Kostyshyna, Olena.; Federal Reserve Bank of St. Louis.
Series: Working paper -- 2007-007A; Working paper (Federal Reserve Bank of St. Louis : Online) -- 2007-007A.
Publisher: Federal Reserve Bank of St. Louis
Published: 2007
Place of publication: [St. Louis, Mo.]
Language: eng
Format: Electronic resource
Description: "We analyze the effects of social learning in a widely-studied monetary policy context.  Social learning might be viewed as more descriptive of actual learning behavior in complex market economies.  Ideas about how best to forecast the economy's state vector are initially heterogeneous.  Agents can copy better forecasting techniques and discard those techniques which are less successful.  We seek to understand whether the economy will converge to a rational expectations equilibrium under this more realistic learning dynamic.  A key result from the literature in the version of the model we study is that the Taylor Principle governs both the uniqueness and the expectational stability of the rational expectations equilibrium when all agents learn homogeneously using recursive algorithms.  We find that the Taylor Principle is not necessary for convergence in a social learning context.  We also contribute to the use of genetic algorithm learning in stochastic environments"--Federal Reserve Bank of St. Louis web site.
Notes: Title from PDF file as viewed on 4/19/2007.

Includes bibliographical references.

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