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- 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
- Consumption and Income
- 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.