A Comparative Simulation Study of the Heteroscedasticity Consistent Covariance Matrix Estimators in the Linear Regression Model
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Date
2008-06
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Addis Ababa University
Abstract
III the context of econometric methods of estimation the variances of OLS estimates
derived under the assumption of homoscedasticity are not consistent when there is
heteroscedasticity and their use can lead to incorrect inferences. Thus, this paper sets out
to examine the performance of several modified versions of heteroscedasticity consistent
covariance matrix (HCCM) estimator (namely HCO, HC I , HC2, and HC3) of White
(1980) and white and MackiJU10n (1985) over a range of sample sizes. Most applications
that use HCCM appear to rely on HCO, yet tests based on the other HCCM estimators are
found to be consistent even in the presence of heteroscedasticity of an unknown form .
Based on Monte Carlo experiments which compare the performance of the t statistic, it
was found out that HC2 and HC3 estimators precisely out perform the others in small
samples. In particular HC3 estimator for samples of size less than 100 was found to be
better than the other HCCM estimators; when samples are 250 or larger, other versions of
the HCCM can be used. Added to that, it was cost advantageous to employ HC3 instead
of ordinary least square covariance matrix (OLSCM) even when there is li ttle evidence of
hetreoscedastici ty.
Key words
White estimator, Monte Carlo Simulation, Linear Regression, Heterosexuality
Description
Keywords
White estimator, Monte Carlo Simulation, Linear Regression, Heteroscedasticlty