A Comparative Simulation Study of the Heteroscedasticity Consistent Covariance Matrix Estimators in the Linear Regression Model

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Addis Ababa University


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



White estimator, Monte Carlo Simulation, Linear Regression, Heteroscedasticlty