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Lecture Slides:      Powerpoint     PDF

Learning Objectives

• Demonstrate the problem of heteroskedasticity and its implications
• Conduct and interpret tests for heteroscedasticity
• Correct for heteroscedasticity using White’s heteroskedasticity-robust estimator
• Correct for heteroscedasticity by getting the model right

Examples

1. Moving off the Farm
2. Carbon Emissions

What We Learned

• Heteroskedasticity means that the error variance is different for some values of X than for others; it can indicate that the model is misspecified.
• Heteroskedasticity causes OLS to lose its “best” property and it causes the standard error formula to be wrong (i.e., estimated standard errors are biased).
• The standard errors can be corrected with White’s heteroskedasticity-robust estimator.
• Getting the model right by, for example, taking logs can sometimes eliminate the heteroskedasticity problem.