Inference under Heteroscedasticity of Unknown Form Using an Adaptive Estimator

The heteroscedasticity consistent covariance matrix estimators are commonly used for the testing of regression coefficients when error terms of regression model are heteroscedastic. These estimators are based on the residuals obtained from the method of ordinary least squares and this method yields...

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Bibliographic Details
Published in:Communications in statistics. Theory and methods 2011-12, Vol.40 (24), p.4431-4457
Main Authors: Ahmed, Munir, Aslam, Muhammad, Pasha, G. R.
Format: Article
Language:English
Subjects:
Adaptive estimator
Estimated weighted least squares
Estimators
Exact sciences and technology
General topics
Global analysis, analysis on manifolds
HCCME
Heteroscedasticity-consistent interval estimator
Kernel weighted least squares
Least squares method
Linear inference, regression
Mathematical analysis
Mathematical models
Mathematics
Null rejection rate
Parametric inference
Probability and statistics
Regression
Samples
Sciences and techniques of general use
Size distortion
Statistical analysis
Statistics
Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds
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Summary:The heteroscedasticity consistent covariance matrix estimators are commonly used for the testing of regression coefficients when error terms of regression model are heteroscedastic. These estimators are based on the residuals obtained from the method of ordinary least squares and this method yields inefficient estimators in the presence of heteroscedasticity. It is usual practice to use estimated weighted least squares method or some adaptive methods to find efficient estimates of the regression parameters when the form of heteroscedasticity is unknown. But HCCM estimators are seldom derived from such efficient estimators for testing purposes in the available literature. The current article addresses the same concern and presents the weighted versions of HCCM estimators. Our numerical work uncovers the performance of these estimators and their finite sample properties in terms of interval estimation and null rejection rate.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2010.513793