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...
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| Format: | Article |
| Language: | English |
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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 |
| _version_ | 1797198418139938816 |
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| author | Alfredas Račkauskas Danas Zuokas |
| author_facet | Alfredas Račkauskas Danas Zuokas |
| author_sort | Alfredas Račkauskas |
| collection | DOAJ |
| description |
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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| first_indexed | 2024-03-07T14:22:06Z |
| format | Article |
| id | doaj.art-2afe0469faf245e99bff05db9a429e02 |
| institution | Directory Open Access Journal |
| issn | 0132-2818 2335-898X |
| language | English |
| last_indexed | 2024-04-24T06:59:32Z |
| publishDate | 2023-09-01 |
| publisher | Vilnius University Press |
| record_format | Article |
| series | Lietuvos Matematikos Rinkinys |
| spelling | doaj.art-2afe0469faf245e99bff05db9a429e022024-04-22T09:00:48ZengVilnius University PressLietuvos Matematikos Rinkinys0132-28182335-898X2023-09-0146spec.10.15388/LMR.2006.30725Properties of the coefficient estimators for the linear regression model with heteroskedastic error termAlfredas Račkauskas0Danas Zuokas1Vilnius UniversityVilnius University 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. https://www.zurnalai.vu.lt/LMR/article/view/30725heteroskedasticitychanged segmentHölder norm tests |
| spellingShingle | Alfredas Račkauskas Danas Zuokas Properties of the coefficient estimators for the linear regression model with heteroskedastic error term Lietuvos Matematikos Rinkinys heteroskedasticity changed segment Hölder norm tests |
| title | Properties of the coefficient estimators for the linear regression model with heteroskedastic error term |
| title_full | Properties of the coefficient estimators for the linear regression model with heteroskedastic error term |
| title_fullStr | Properties of the coefficient estimators for the linear regression model with heteroskedastic error term |
| title_full_unstemmed | Properties of the coefficient estimators for the linear regression model with heteroskedastic error term |
| title_short | Properties of the coefficient estimators for the linear regression model with heteroskedastic error term |
| title_sort | properties of the coefficient estimators for the linear regression model with heteroskedastic error term |
| topic | heteroskedasticity changed segment Hölder norm tests |
| url | https://www.zurnalai.vu.lt/LMR/article/view/30725 |
| work_keys_str_mv | AT alfredasrackauskas propertiesofthecoefficientestimatorsforthelinearregressionmodelwithheteroskedasticerrorterm AT danaszuokas propertiesofthecoefficientestimatorsforthelinearregressionmodelwithheteroskedasticerrorterm |