Properties of the coefficient estimators for the linear regression model with heteroskedastic error term
In: Lietuvos Matematikos Rinkinys, Jg. 46 (2023-09-01), Heft spec.
Online
academicJournal
Zugriff:
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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Properties of the coefficient estimators for the linear regression model with heteroskedastic error term
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Autor/in / Beteiligte Person: | Račkauskas, Alfredas ; Zuokas, Danas |
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Zeitschrift: | Lietuvos Matematikos Rinkinys, Jg. 46 (2023-09-01), Heft spec. |
Veröffentlichung: | Vilnius University Press, 2023 |
Medientyp: | academicJournal |
ISSN: | 0132-2818 (print) ; 2335-898X (print) |
DOI: | 10.15388/LMR.2006.30725 |
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