Chapter 8 - Heteroskedasticity

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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


  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.