Introducing the White’s Heteroskedasticity Consistent Estimator

An introduction to the HC estimator, and its importance in building regression models in the face of heteroskedasticity

In this article, we’ll bring together two fundamental topics in statistical modeling, namely the covariance matrix and heteroskedasticity.

Covariance matrices are the work horses of statistical inference. They are used for determining if regression coefficients are statistically significant (i.e. different from zero), and for constructing confidence intervals for each coefficient. To do this work, they make a few crucial assumptions. Chief among …

consistent covariance-matrix heteroskedasticity linear regression regression thoughts-and-theory