ABSTRACT
In Chapter 2 we briefly examined the problem of autocorrelation and the disruptive effects it can have on our regression model. Autocorrelation is a violation of one of the basic assumptions of the OLS regression model, which requires that the error terms are independent of each other. To the extent that this condition is breached, the model suffers in its reliability and accuracy.
