4. Heteroskedasticity and Autocorrelation of Errors

The regression model is a random model in the sense that an error term is included in the equation linking the dependent variable to the explanatory variables. The ordinary least squares method—the most frequently used estimation method—supposes (i) the absence of autocorrelation of errors and (ii) the homoskedasticity of errors, i.e., the fact that the variance of the errors is constant. When this second assumption is violated, we speak of heteroskedasticity: the variance of the errors is no longer constant. This chapter concentrates on the problems of autocorrelation and heteroskedasticity of errors. It presents the appropriate estimation methods, as well as the sources, tests, and solutions to autocorrelation and heteroskedasticity. It also provides several empirical applications to illustrate the various theoretical concepts.