Parameter estimation in a stationary autoregressive process with correlated multiple observations

S. Sethuraman, I. V. Basawa

Research output: Contribution to journalArticle

5 Scopus citations


An autoregressive process is proposed to model time series data with multiple observations at each time point. The joint autocorrelation function for the model has a product form, the first factor being the autocorrelation function for a stationary AR(p) process and the second factor involving a constant intraclass correlation ρ. The least-squares and the Gaussian maximum likelihood estimators of the autoregression parameters θ=(θ1,...,θp)T and the intraclass correlation ρ are presented and their limit distributions are derived.

Original languageEnglish (US)
Pages (from-to)137-154
Number of pages18
JournalJournal of Statistical Planning and Inference
Issue number2
StatePublished - Apr 15 1994
Externally publishedYes



  • Intraclass correlation
  • Panel time series
  • asymptotic distributions
  • least-squares estimation
  • maximum likelihood estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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