Consistency, asymptotic unbiasedness and bounds on the bias of s2 in the linear regression model with error component disturbances

B. H. Baltagi; W. Krämer

doi:10.1007/BF02926424

Consistency, asymptotic unbiasedness and bounds on the bias of s² in the linear regression model with error component disturbances

B. H. Baltagi, W. Krämer

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

The OLS estimator of the disturbance variance in the linear regression model with error component disturbances is shown to be weakly consistent and asymptotically unbiased without any restrictions on the regressor matrix. Also, simple exact bounds on the expected value of s² are given for both the one-way and two-way error component models.

Original language	English (US)
Pages (from-to)	323-328
Number of pages	6
Journal	Statistical Papers
Volume	35
Issue number	1
DOIs	https://doi.org/10.1007/BF02926424
State	Published - Dec 1994
Externally published	Yes

Keywords

Bounds on Bias
Error Components Models
Variance Estimation

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

Access to Document

10.1007/BF02926424

Cite this

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abstract = "The OLS estimator of the disturbance variance in the linear regression model with error component disturbances is shown to be weakly consistent and asymptotically unbiased without any restrictions on the regressor matrix. Also, simple exact bounds on the expected value of s2 are given for both the one-way and two-way error component models.",

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