By autocorrelation in a regression model, we mean that the error term in this period depends on its value in previous periods. We will begin by looking at the Breusch-Godfrey test for autocorrelation. If we find that autocorrelation is present then the standard errors from OLS are no longer useful and we will look at robust standard errors. If it can be assumed that the error terms follow an AR(1) process, then it is possible to replace OLS with an efficient estimator.