On the asymptotic relative efficiency of gaussian and least squares estimators for vector arma models

D. S. Poskitt; M. O. Salau

doi:10.1006/jmva.1994.1064

On the asymptotic relative efficiency of gaussian and least squares estimators for vector arma models

D. S. Poskitt, M. O. Salau

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

6 Citations (Scopus)

Abstract

This paper is concerned with the asymptotic relative efficiency of the Gaussian and least squares estimators when employed to estimate the parameters of vector ARMA models presented in echelon canonical form. The relative efficiency is assessed via the variance-covariance matrices of the limiting normal distributions of the two estimators. Situations under which substantial loss or gain in efficiency could be exprected are discussed and illustrated with some numerical examples.

Original language	English
Pages (from-to)	294-317
Number of pages	24
Journal	Journal of Multivariate Analysis
Volume	51
Issue number	2
DOIs	https://doi.org/10.1006/jmva.1994.1064
Publication status	Published - 1 Jan 1994
Externally published	Yes

Keywords

Asymptotic relative efficiency
Central limit theorem
Echelon canonical form
Gaussian estimator
Kronecker indices
Least squares estimator
Vector time series models

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10.1006/jmva.1994.1064

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KW - Central limit theorem

KW - Echelon canonical form

KW - Gaussian estimator

KW - Kronecker indices

KW - Least squares estimator

KW - Vector time series models

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On the asymptotic relative efficiency of gaussian and least squares estimators for vector arma models

Abstract

Keywords

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