### 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 language | English (US) |
---|---|

Pages (from-to) | 687-713 |

Number of pages | 27 |

Journal | Journal of Economic Dynamics and Control |

Volume | 15 |

Issue number | 4 |

DOIs | |

State | Published - 1991 |

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### 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.

Research output: Contribution to journal › Article

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TY - JOUR

T1 - On the convergence of Bayesian posterior processes in linear economic models Counting equations and unknowns

AU - Nyarko, Yaw

PY - 1991

Y1 - 1991

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0000823517&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0000823517&partnerID=8YFLogxK

U2 - 10.1016/0165-1889(91)90039-4

DO - 10.1016/0165-1889(91)90039-4

M3 - Article

VL - 15

SP - 687

EP - 713

JO - Journal of Economic Dynamics and Control

JF - Journal of Economic Dynamics and Control

SN - 0165-1889

IS - 4

ER -