A Bayesian agent relies on past observations to learn the structure of a stationary process. We show that the agent's predictions about near-horizon events become arbitrarily close to those he would have made if he knew the long-run empirical frequencies of the process.
|Original language||English (US)|
|Number of pages||9|
|Journal||Games and Economic Behavior|
|State||Published - May 2018|
ASJC Scopus subject areas
- Economics and Econometrics