Autoregressive-output-analysis methods revisited

Mingjian Yuan*, Barry L. Nelson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


We revisit and update the autoregressive-output-analysis method for constructing a confidence interval for the steady-state mean of a simulated process by using Rissanen's predictive least-squares criterion to estimate the autoregressive order of the process. This order estimator is strongly consistent when the output is autoregressive. The order estimator is combined with the standard autoregressive-output-analysis method to form a confidence-interval procedure. Alternatives for estimating the degrees of freedom for the procedure are investigated. The main result is an asymptotically valid confidence-interval procedure that, empirically, has good small-sample properties.

Original languageEnglish (US)
Pages (from-to)391-418
Number of pages28
JournalAnnals of Operations Research
Issue number1
StatePublished - Dec 1 1994


  • Autoregressive process
  • confidence interval
  • output analysis
  • simulation
  • statistics
  • time series

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Management Science and Operations Research


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