Learning the fundamentals in a stationary environment

Nabil I. Al-Najjar*, Eran Shmaya

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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 languageEnglish (US)
Pages (from-to)616-624
Number of pages9
JournalGames and Economic Behavior
StatePublished - May 2018


  • Learning
  • Merging
  • Stationarity

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics


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