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

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 - Oct 1991

ASJC Scopus subject areas

  • Economics and Econometrics
  • Control and Optimization
  • Applied Mathematics

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