Title: Heteroscedasticity: as nasty as it sounds?
Course: UGQuants (SSPS) > Section: Multiple Regression

Heteroscedasticity: as nasty as it sounds?

It is quite common in multiple regression that the residuals increase or decrease with the value of the dependent variable.  This can cause problems because it can bias the standard error of the residuals which are used in the calculation of coefficients and thus lead to a model that is poorer than it need be, or which inflates the significance of some of the coefficients.  This is described as heteroscedasticity, the opposite of ‘homoscedasticity’ (residuals that are uncorrelated with the dependent variable).

There are a number of ways of trying to deal with this problem, one of which is to transform the dependent variable to a more normal distribution.  It might also be that you would benefit from including more independent variables in the model, but only if they make sense in relation to the theory you are trying to develop or test.