Does co-integration help long-term forecasts? In this paper, we use simulation, real data sets, and multi-step-ahead post-sample forecasts to study this question. Based on the square root of the trace of forecasting error-covariance matrix, we found that for simulated data imposing the correct unit-root constraints implied by co-integration does improve the accuracy of forecasts. For real data sets, the answer is mixed. Imposing unit-root constraints suggested by co-integration tests produces better forecasts for some cases, but fares poorly for others. We give some explanations for the poor performance of co-integration in long-term forecasting and discuss the practical implications of the study. Finally, an adaptive forecasting procedure is found to perform well in one- to ten-step-ahead forecasts.
