A general condition for an optimal limiting efficiency of OLS in the general linear regression model

Walter Krämer; Badi Baltagi

doi:10.1016/0165-1765(95)00716-4

A general condition for an optimal limiting efficiency of OLS in the general linear regression model

Walter Krämer, Badi Baltagi

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

6 Scopus citations

Abstract

We give a general condition for disturbance covariance matrices in the general linear regression model which ensures that, in the limit, ordinary least squares is as efficient as generalized least squares as the disturbance covariance matrix approaches the edges of the parameter space. This condition includes many known efficiency results as special cases.

Original language	English (US)
Pages (from-to)	13-17
Number of pages	5
Journal	Economics Letters
Volume	50
Issue number	1
DOIs	https://doi.org/10.1016/0165-1765(95)00716-4
State	Published - Jan 1996
Externally published	Yes

Keywords

Efficiency of OLS
Generalized least squares
Linear regression

ASJC Scopus subject areas

Finance
Economics and Econometrics

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10.1016/0165-1765(95)00716-4

Cite this

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title = "A general condition for an optimal limiting efficiency of OLS in the general linear regression model",

abstract = "We give a general condition for disturbance covariance matrices in the general linear regression model which ensures that, in the limit, ordinary least squares is as efficient as generalized least squares as the disturbance covariance matrix approaches the edges of the parameter space. This condition includes many known efficiency results as special cases.",

keywords = "Efficiency of OLS, Generalized least squares, Linear regression",

author = "Walter Kr{\"a}mer and Badi Baltagi",

year = "1996",

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AU - Baltagi, Badi

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AB - We give a general condition for disturbance covariance matrices in the general linear regression model which ensures that, in the limit, ordinary least squares is as efficient as generalized least squares as the disturbance covariance matrix approaches the edges of the parameter space. This condition includes many known efficiency results as special cases.

KW - Efficiency of OLS

KW - Generalized least squares

KW - Linear regression

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A general condition for an optimal limiting efficiency of OLS in the general linear regression model

Abstract

Keywords

ASJC Scopus subject areas

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