Convergence of least squares learning in self-referential discontinuous stochastic models

Research output: Contribution to journalArticle

Abstract

We examine the stability of rational expectations equilibria in the class of models in which the decision of the individual agent is discontinuous with respect to the state variables. Instead of rational expectations, each agent learns the unknown parameters through a recursive stochastic algorithm. If the agents update the estimated value function rapidly enough, then each agent learns the true value function associated with the optimal action with probability 1 and almost always takes the optimal action asymptotically. Journal of Economic Literature Classification Number: C62, D83.

Original languageEnglish
Pages (from-to)78-114
Number of pages37
JournalJournal of Economic Theory
Volume101
Issue number1
DOIs
StatePublished - 2001 Jan 1

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Least squares learning
Stochastic model
Value function
Economics
State variable
Rational expectations equilibrium
Rational expectations

Keywords

  • Discontinuous decision rule
  • Rational expectations
  • Recursive learning
  • Search
  • Stochastic approximation

Cite this

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Convergence of least squares learning in self-referential discontinuous stochastic models. / Cho, In Koo.

In: Journal of Economic Theory, Vol. 101, No. 1, 01.01.2001, p. 78-114.

Research output: Contribution to journalArticle

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