– in order to reduce the variance of observations with extreme values
– e.g. by taking logarithms or by scaling some variables
2. Weighted Least Squares (WLS)
• consider the model
• Suppose
• If we redefine the model as
it becomes homoscedastic
3.Heterosticity-corrected robust standard errors
• The logic behind: since heteroskedasticity causes problems with the standard errors of OLS but not with the coefficients, it makes sense to improve the estimation of the standard errors in a way that does not alter the estimate of the coefficients (White, 1980)