Properties of the coefficient estimators for the linear regression model with heteroskedastic error term
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 empir...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Vilnius University Press
2023-09-01
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| Series: | Lietuvos Matematikos Rinkinys |
| Subjects: | |
| Online Access: | https://www.zurnalai.vu.lt/LMR/article/view/30725 |
| Summary: | 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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| ISSN: | 0132-2818 2335-898X |