Parameter estimation in regression models with autocorrelated errors using irregular data

Dong Wan Shin, Sahadeb Sarkar

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


We consider the least squares and the Gaussian maximum likelihood estimators in the regression model with stochastic explanatory variables and autocorrelated errors, possibly nonnormal, in the situation where data contain irregular observations or missing values. We establish the weak consistency and asymptotic normality of the estimators. We compare the efficiency of the least squares estimator of the regression parameter to that of the maximum likelihood estimator for a special case of the model.

Original languageEnglish
Pages (from-to)3567-3580
Number of pages14
JournalCommunications in Statistics - Theory and Methods
Issue number12
StatePublished - 1 Jan 1994

Bibliographical note

Funding Information:
The research of the first author was supported by a Grant from Korea Science and Engineering Foundation, and the research of the second author was supported in part by a Grant from the Arts and Sciences Research Fund at Oklahoma State University.


  • asymptotic normality
  • autocorrelated errors
  • consistency
  • incomplete data
  • lead squares estimator
  • maximum lilcelihood estimator
  • Regression


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