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Properties of the coefficient estimators for the linear regression model with heteroskedastic error term
- Source :
- Lietuvos Matematikos Rinkinys, Vol 46, Iss spec. (2023)
- Publication Year :
- 2023
- Publisher :
- Vilnius University Press, 2023.
-
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.
- Subjects :
- heteroskedasticity
changed segment
Hölder norm tests
Mathematics
QA1-939
Subjects
Details
- Language :
- English, Lithuanian
- ISSN :
- 01322818 and 2335898X
- Volume :
- 46
- Issue :
- spec.
- Database :
- Directory of Open Access Journals
- Journal :
- Lietuvos Matematikos Rinkinys
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.2afe0469faf245e99bff05db9a429e02
- Document Type :
- article
- Full Text :
- https://doi.org/10.15388/LMR.2006.30725