COINTEGRATION CONSTRAINT AND FORECASTING - AN EMPIRICAL-EXAMINATION

Does co-integration help long-term forecasts? In this paper, we use si mulation, real data sets, and multistep-ahead post-sample forecasts to study this question. Based on the square root of the trace of forecas ting error-covariance matrix, we found that for simulated data imposin g the 'correct' unit-root constraints implied by co-integration does i mprove the accuracy of forecasts. For real data sets, the answer is mi xed. 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 longterm 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.