Chapter 9 - Correlated Errors

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Lecture Slides:      Powerpoint     PDF

Learning Objectives

  • Demonstrate the problem of correlated errors and its implications
  • Conduct and interpret tests for correlated errors
  • Correct for correlated errors using Newey and West’s estimator (ex post) or using generalized least squares (ex ante)
  • Correct for correlated errors by adding lagged variables to the model
  • Show that correlated errors can arise in clustered and spatial data as well as in time-series data


  1. Consumption and Income
  2. Oil Prices

What We Learned

  • Correlated errors cause OLS to lose its “best” property and the estimated standard errors to be biased.
    • Same as heteroscedasticity
  • As long as the autocorrelation is not too strong, the standard error bias can be corrected with Newey and West’s heteroskedasticity and autocorrelation consistent estimator.
  • Getting the model right by adding lagged variables to the model is usually the best approach to deal with autocorrelation in time-series data.