I read that OLS underestimates variance when residuals are autocorrelated. I see why autocorrelation would be a problem in time series analysis, in the sense that the coefficient are not efficient because we're not including all the potential predictors. But is there a mathematical problem as well?
For example, we want to predict the used-cars sales margins. The data set includes each vehicle make, model, mileage p/gallon, price, options etc. and the final sale price. For some reason the catalog has been sorted by car make and year/model, so adjacent observations will likely have similar sale numbers. Is autocorrelation a problem in this case?