Chapter 11
Heteroskedasticity
In This Chapter
Understanding the difference between homoskedasticity and heteroskedasticity
Uncovering the consequences of heteroskedasticity
Identifying harmful heteroskedasticity
Fixing heteroskedasticity problems
As I explain in Chapter 6, a critical assumption of the classical linear regression model is homoskedasticity — that the variance of the error term is constant over various values of the independent variables. However, this assumption may not always hold. When it doesn’t happen, you have heteroskedasticity. This chapter shows you how to determine whether you have heteroskedasticity in a particular application and what you can do to remedy it if you do.
Distinguishing between Homoskedastic and Heteroskedastic Disturbances
The error term is the most important component of the classical linear regression model (CLRM). Most of the CLRM assumptions that allow econometricians to prove the desirable properties of the OLS estimators (the Gauss-Markov theorem) directly involve characteristics about the error term (or disturbances). One of the CLRM ...
Get Econometrics For Dummies now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
