Discussion
Which of the following are plausible approaches to dealing with a model that exhibits 'heteroscedasticity' ?
(i) Take logarithms of each of the variables
(ii) Use suitably modified standard errors
(iii) Use generalised least squares procedure
(iv) Add logged values of the variables to the regression equations
Answer: C
The plausible approaches are (i), (ii) and (iii) only.
Taking logarithms of the variables often reduces heteroscedasticity, because it compresses the scale on which the error variance grows (i). Heteroscedasticity-consistent (White) standard errors keep the OLS estimates but correct the inference (ii). Generalised least squares, or weighted least squares when the form of the variance is known, transforms the model so that the errors become homoscedastic (iii).
Adding logged values of the variables as extra regressors (iv) changes the specification of the conditional mean but does nothing to the error variance, so it is not a remedy for heteroscedasticity. That rules out every option containing (iv).
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