Parameter estimation in a stationary autoregressive process with correlated multiple observations

S. Sethuraman; I. V. Basawa

doi:10.1016/0378-3758(94)90203-8

Parameter estimation in a stationary autoregressive process with correlated multiple observations

S. Sethuraman, I. V. Basawa

Research output: Contribution to journal › Article › peer-review

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,...,θ_p)^T and the intraclass correlation ρ are presented and their limit distributions are derived.

Original language	English (US)
Pages (from-to)	137-154
Number of pages	18
Journal	Journal of Statistical Planning and Inference
Volume	39
Issue number	2
DOIs	https://doi.org/10.1016/0378-3758(94)90203-8
State	Published - Apr 15 1994
Externally published	Yes

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

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Cite this

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title = "Parameter estimation in a stationary autoregressive process with correlated multiple observations",

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.",

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AU - Sethuraman, S.

AU - Basawa, I. V.

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N2 - 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.

AB - 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.

KW - Intraclass correlation

KW - Panel time series

KW - asymptotic distributions

KW - least-squares estimation

KW - maximum likelihood estimation

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EP - 154

JO - Journal of Statistical Planning and Inference

JF - Journal of Statistical Planning and Inference

SN - 0378-3758

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