The Effect of Estimating Parameters onLong-Term Forecasts for CointegratedSystems
The Effect of Estimating Parameters onLong-Term Forecasts for CointegratedSystems
复制标题
估计参数对协整系统长期预测的影响
DOI：
10.1002/for.1230
复制
发表时间：
2012
影响因子：
3.4
通讯作者：
Y
中科院分区：
文献类型：
--
作者：
Chigira;H. and Taku;Y
本文涉及协整过程的长期预测。首先，考虑了模型参数已知的情况。本文的分析表明，协整和整合约束在长期预测中都不重要。它是对协整过程的长期预测的另一种含义，扩展了以前有影响力的研究的结果。适当的Mote-Carlo实验支持我们的分析结果。其次，也是更重要的是，考虑了模型参数估计的情况。结果表明，在长期预报中，漂移项估计的准确性是至关重要的。也就是说，各种长期预测的相对精度取决于漂移项估计值的方差的相对大小。进一步的实验表明，在有限样本下，用差分数据的简单时间平均估计漂移项的单变量ARIMA预测优于用著名的Johansen‘s ML方法估计参数的协整系统预测。在有限样本实验的基础上，推荐使用单变量ARIMA预测，而不是传统的有限样本协整系统预测，因为它具有实用性和对模型错误的稳健性。版权所有©2011 John Wiley&Sons，Ltd.
This paper concerns Long‐term forecasts for cointegrated processes. First, it considers the case where the parameters of the model are known. The paper analytically shows that neither cointegration nor integration constraint matters in Long‐term forecasts. It is an alternative implication of Long‐term forecasts for cointegrated processes, extending the results of previous influential studies. The appropriate Mote Carlo experiment supports our analytical result. Secondly, and more importantly, it considers the case where the parameters of the model are estimated. The paper shows that accuracy of the estimation of the drift term is crucial in Long‐term forecasts. Namely, the relative accuracy of various Long‐term forecasts depends upon the relative magnitude of variances of estimators of the drift term. It further experimentally shows that in finite samples the univariate ARIMA forecast, whose drift term is estimated by the simple time average of differenced data, is better than the cointegrated system forecast, whose parameters are estimated by the well‐known Johansen's ML method. Based upon finite sample experiments, it recommends the univariate ARIMA forecast rather than the conventional cointegrated system forecast in finite samples for its practical usefulness and robustness against model misspecifications. Copyright © 2011 John Wiley & Sons, Ltd.