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1968•Journal of the American Statistical AssociationRequires access

Grouping Estimators on Heteroscedastic Data

Tony Lancaster

Open publisher page 15 citations

Abstract

This paper gives numerical comparisons of the efficiency of Ordinary Least Squares (OLS) and Grouping Estimators in simple linear regression. The disturbances are assumed to have unequal variances, and an assumption is made about the form of this heteroscedasticity. It is shown that for some types of heteroscedasticity a Grouping Estimator can be more efficient than Ordinary Least Squares.

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What this paper is about

This paper gives numerical comparisons of the efficiency of Ordinary Least Squares (OLS) and Grouping Estimators in simple linear regression. The disturbances are assumed to have unequal variances, and an assumption is made about the form of this heteroscedasticity. It is shown that for some types of heteroscedasticity a Grouping Estimator can be more efficient than Ordinary Least Squares.

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OpenAlex reports 15 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper gives numerical comparisons of the efficiency of Ordinary Least Squares (OLS) and Grouping Estimators in simple linear regression. The disturbances are assumed to have unequal variances, and an assumption is made about the form of this heteroscedasticity. It is shown that for some types of heteroscedasticity a Grouping Estimator can be more efficient than Ordinary Least Squares.

Key concepts: Heteroscedasticity, Ordinary least squares, Estimator, Mathematics, Statistics, Econometrics, Simple linear regression, Generalized least squares

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