Structural inference for linear regression with autocorrelated errors

Research output: Contribution to journalArticlepeer-review


Methods of structural inference are applied to the linear regression model in which the errors follow an autoregressive process. A marginal likelihood function is derived for the autoregressive parameters while structural distributions are obtained for the regression parameters. The marginal likelihood function, in the case of a Markov error process, is shown to be related under certain conditions to the Durbin-Watsonstatistic. This method of inference is illustrated by a simulated example.

Original languageEnglish
Pages (from-to)85-104
Number of pages20
JournalStatistische Hefte
Issue number2
StatePublished - Jun 1975
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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