In summary, to ensure efficient inference of the regression parameters and the regression function, the heteroscedasticity must be accounted for.
12.
It might also reveal outliers, heteroscedasticity, and other aspects of the data that may complicate the interpretation of a fitted regression model.
13.
Equivalently, heteroscedasticity refers to unequal conditional variances in the response variables " Y " " i ", such that
14.
Most of the methods of detecting heteroscedasticity outlined above can be modified for use even when the data do not come from a normal distribution.
15.
This provides White's ( 1980 ) estimator, often referred to as " HCE " ( heteroscedasticity-consistent estimator ):
16.
In the case of a linear model with a working independence variance structure, these are known as " heteroscedasticity consistent standard error " estimators.
17.
While WLS assumes independence of observations it does not assume equal variance and is therefore a solution for parameter estimation in the presence of heteroscedasticity.
18.
Some authors refer to this as conditional heteroscedasticity to emphasize the fact that it is the sequence of conditional variances that changes and not the unconditional variance.
19.
An initial scatter plot of the data indicates that there is heteroscedasticity in the data as the variance is not constant at each level of the predictor.
20.
For instance, while the ordinary least squares estimator is still unbiased in the presence of heteroscedasticity, it is inefficient because the true variance and covariance are underestimated.
