Examining and Correcting for Heteroskedasticity using Stata

Heteroskedasticity is frequently encountered in regression analysis. It is something that we need to routinely examine for in each model we run, since it's presence will produce results that lead us to make erroneous inferences with our hypothesis tests.

We will use the State Crimel data set for this analysis. StateCrime2.dta

The steps we will follow will be:

• Run the regression analysis
• Test for heteroskedasticity
• Run the regression model with robust standard errors
• Examine the residuals
• Find a creative solution

Run the regression analysis

Using the state crime data regress the crime rate on the employment and urbanization rates.

regress rate96 urb96 emprat96

Note that heteroskedasticity is not apparent based on anything displayed in the analysis. We therefore need to test for it.

Test for heteroskedasticity

Testing for heteroskedasticity in Stata is quite easy. Use the following command directly after the regression run.

hettest

Run the regression model with robust standard errors

regress rate96 urb96 emprat96, r

Examine the residuals

Save the residuals with

predict res, r

The plot the residuals by the dependent variable

plot res rate96

Find a creative solution