On the convergence of Bayesian posterior processes in linear economic models Counting equations and unknowns

Yaw Nyarko

    Research output: Contribution to journalArticle

    Abstract

    I propose a technique, counting 'equations' and 'unknowns', for determining when the posterior distributions of the parameters of a linear regression process converge to their true values. This is applied to examples and to the infinite-horizon optimal control of this linear regression process with learning, and in particular to the problem of a monopolist seeking to maximize profits with unknown demand curve. Such a monopolist has a tradeoff between choosing an action to maximize the current-period reward and to maximize the information value of that action. I use the above technique to determine the monopolist's limiting behavior and to determine whether in the limit it learns the true parameter values of the demand curve.

    Original languageEnglish (US)
    Pages (from-to)687-713
    Number of pages27
    JournalJournal of Economic Dynamics and Control
    Volume15
    Issue number4
    DOIs
    StatePublished - 1991

    Fingerprint

    Economic Model
    Linear regression
    Counting
    Linear Model
    Maximise
    Unknown
    Economics
    Value of Information
    Profitability
    Curve
    Limiting Behavior
    Infinite Horizon
    Posterior distribution
    Reward
    Profit
    Optimal Control
    Trade-offs
    Converge
    Monopolist
    Demand

    ASJC Scopus subject areas

    • Economics and Econometrics
    • Control and Optimization

    Cite this

    On the convergence of Bayesian posterior processes in linear economic models Counting equations and unknowns. / Nyarko, Yaw.

    In: Journal of Economic Dynamics and Control, Vol. 15, No. 4, 1991, p. 687-713.

    Research output: Contribution to journalArticle

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