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- 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
- Moving off the Farm
- 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.