Regression models are usually estimated using Ordinary Least Squares (OLS). An alternative method is to minimize the sum of absolute errors between the actual observation and its “predicted” (fitted) value for Monte Carlo simulation studies, Dielman concluded that, in cases in which outliers are expected, LAV provides better forecasts than does least squares and is nearly as accurate as least squares for data that have normally distributed errors.