Monte Carlo analysis of the performance of alternative estimators of simultaneous system's coefficients in the presence of autocorrelation is performed. The "true" underlying model is an estimated, three-equation, monthly model of the U.S. pork market. Estimators for ex post forecasts are also compared. Multicollinearity is found to be a salient characteristic likely adversely affecting estimator performance. Results show that correcting for autocorrelation is desirable when levels of autocorrelation are high for both parameter accuracy and ex post forecasting. However, the best structural coefficient estimator for high levels of autocorrelation is not necessarily the best estimator for ex post forecasting.