Parameter estimation in a stationary autoregressive process with correlated multiple observations

S. Sethuraman, I. V. Basawa

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

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
Volume39
Issue number2
DOIs
StatePublished - Apr 15 1994
Externally publishedYes

Keywords

  • 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

Fingerprint Dive into the research topics of 'Parameter estimation in a stationary autoregressive process with correlated multiple observations'. Together they form a unique fingerprint.

  • Cite this