Heteroskedastic Consistent (HC) Covariance Estimators
EViews 10 increases the options for heteroskedastic consistent covariance estimators beyond the familiar White estimator available in previous versions. The class of estimators supported belong to the HC family described by Long and Ervin, 2000, and Cribari-Neto and da Silva, 2011.
The estimators differ in their choice of observation-specific weights used to improve the finite sample properties of the residual error covariance.
Specifically, EViews supports the following estimators and weight choices:
|HC0 – White||1|
|HC1 – White with d.f. correction||√T/(T−k)|
|HC2 – bias corrected||(1−ht)−1/2|
|HC3 – pseudo-jacknife||(1−ht)−1|
|HC4 – relative leverage||(1−ht)−δt/2|
|User – user specified||arbitrary|
where ht=X⊤t(X⊤X)−1Xt are the diagonal elements of the familiar “hat matrix” H=X⊤(X⊤X)−1X, and δt and γt are discount factors.