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March 31, 2005
Heteroskedasticity
Plotting the data or, especially, the residuals vs. one of the regressors is an effective test for heteroskedasticity. The Goldfeld-Quandt test formalizes the concept by looking at the residual variance for the largest third and the smallest third of the values for x.
A simple model of the process generating the heteroskedasticity leads to a simple transformation to homoskedasticity.
Another pattern (not heteroskasticity) might also be apparent from a plot of the residuals. Here we see a classic case of the wrong functional form.
Heteroskeasticity is really important in financial market research when it takes the form of Autoregressive Conditional Heteroskedasticity (ARCH). The prices of assets vary with their risk, which can be viewed as their conditional variance.
Posted by bparke at March 31, 2005 08:58 PM