ABSTRACT
Autocorrelation is a time-series problem that occurs frequently with economic data. Secondorder serial correlation is much less frequently encountered. As you might imagine, second-order serial correlation is when the current error term is related to the two prior error terms: et = p i et_i + P/?2e t -2+£t
11.2 Consequences of Serial Correlation
Serial correlation is a direct violation of assumption 3 of the Classical Linear Regression Model: The error terms are not related to one another: E[u¡ Uj] = 0 for all i ^ j . Recall that assumption 3 was required to prove that the estimators are best, but not unbiased.
