Estimating moving average parameters: Classical pileups and bayesian posteriors

David N. Dejong, Charles H. Whiteman

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


We analyze posterior distributions of the moving average parameter in the first-order case and sampling distributions of the corresponding maximum likelihood estimator. Sampling distributions “pile up” at unity when the true parameter is near unity; hence if one were to difference such a process, estimates of the moving average component of the resulting series would spuriously tend to indicate that the process was overdifferenced. Flat-prior posterior distributions do not pile up, however, regardless of the parameter’s proximity to unity; hence caution should be taken in dismissing evidence that a series has been overdifferenced.

Original languageEnglish (US)
Pages (from-to)311-317
Number of pages7
JournalJournal of Business and Economic Statistics
Issue number3
StatePublished - Jul 1993

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


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