TITLE:
Heteroskedasticity-Consistent Covariance Matrix Estimators in Small Samples with High Leverage Points
AUTHORS:
Esra Şimşek, Mehmet Orhan
KEYWORDS:
Heteroskedasticity-Consistent Covariance Matrix Estimator, HCCME, Robust Estimation, MCD, Monte Carlo, Simulation, Loss Functions
JOURNAL NAME:
Theoretical Economics Letters,
Vol.6 No.4,
July
28,
2016
ABSTRACT: The aim of this paper is to demonstrate the impact of high leverage
observations on the performances of prominent and popular Heteroskedasticity-Consistent
Covariance Matrix Estimators (HCCMEs) with the help of computer simulation. Firstly,
we figure out high leverage observations, then
remove them and recalculate the HCCMEs without these observations in order to compare
the HCCME performances with and without high leverage points. We identify high
leverage observations with the Minimum Covariance Determinant (MCD). We select
from among different covariates and
disturbance term variances from the related literature in simulation runs
in order to compare the percentage difference between the expected value of the
HCCME and true covariance matrix as well as the symmetric loss function. Our
results revealed that the elimination of high leverage (high MCD distance) observations had improved the HCCME
performances considerably and under some settings substantially,
depending on the degree of leverage. We hope our theoretical findings will be
benefited for practical purposes in applications.