Co-integration constraint and forecasting: An empirical examination
通过模拟和真实数据，检验协整约束是否有助于长期预测。结果发现，施加正确的单位根约束能提高模拟数据预测精度，但对真实数据效果不一，并探讨了原因及自适应预测方法的表现。
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.