A Robust Heteroskedasticity Consistent Covariance Matrix Estimator
作者:
Marilena Furno,
期刊:
Statistics
(Taylor Available online 1997)
卷期:
Volume 30,
issue 3
页码: 201-219
ISSN:0233-1888
年代: 1997
DOI:10.1080/02331889708802610
出版商: Gordon & Breach Science Publishers
关键词: Robust estimators;outliers;heteroskedasticity;covariance matrix
数据来源: Taylor
摘要:
To deal with heteroskedasticity of unknown form, this paper suggests to robustly estimate the regression coefficients and then to implement an heteroskedasticity consistent covariance matrix estimator. The robust regression reduces the sample bias of the heteroskedasticity consistent covariance matrix estimator, and does not require the specification of a functional form for heteroskedasticity. Indeed, it replaces each variance with the squared errors, which are in turn estimated by the squared residuals from the robust regression. A Monte-Carlo study verifies the behavior of the proposed estimator, using real data.
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