Properties of the coefficient estimators for the linear regression model with heteroskedastic error term
Articles
Alfredas Račkauskas
Vilnius University
Danas Zuokas
Vilnius University
Published 2023-09-21
https://doi.org/10.15388/LMR.2006.30725
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Keywords

heteroskedasticity
changed segment
Hölder norm tests

How to Cite

Račkauskas, A. and Zuokas, D. (2023) “Properties of the coefficient estimators for the linear regression model with heteroskedastic error term”, Lietuvos matematikos rinkinys, 46(spec.), pp. 267–272. doi:10.15388/LMR.2006.30725.

Abstract

In this paper we present estimated generalized least squares (EGLS) estimator for the coefficient vector β in the linear regression model y = βX + ε, where disturbance term can be heteroskedastic. For the heteroskedasticity of the changed segment type, using Monte-Carlo method, we investigate empirical properties of the proposed and ordinary least squares (OLS) estimators. The results show that the empirical covariance of the EGLS estimators is smaller than that of OLS estimators.

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