Convergence of least squares learning mechanisms in self-referential linear stochastic models

Albert Marcet, Thomas J. Sargent

    Research output: Contribution to journalArticlepeer-review

    Abstract

    We study a class of models in which the law of motion perceived by agents influences the law of motion that they actually face. We assume that agents update their perceived law of motion by least squares. We show how the perceived law of motion and the actual one may converge to one another, depending on the behavior of a particular ordinary differential equation. The differential equation involves the operator that maps the perceived law of motion into the actual one.

    Original languageEnglish (US)
    Pages (from-to)337-368
    Number of pages32
    JournalJournal of Economic Theory
    Volume48
    Issue number2
    DOIs
    StatePublished - Aug 1989

    ASJC Scopus subject areas

    • Economics and Econometrics

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