基于Kullback-Leibler信息量的最优ARMA模型组选择与组合预测研究  

The Best ARMA Model Group Selection and Combined Forecasting Based on Kullback-Leibler Information

作　　者：赵昕东[1] 钱国骐[2] 

机构地区：[1]华侨大学数量经济研究院,福建厦门361021 [2]墨尔本大学数学与统计系

出　　处：《中国管理科学》2011年第5期21-28,共8页Chinese Journal of Management Science

基　　金：福建省自然科学基金项目(2009J01312);中央高校基本科研业务费国家自然科学基金培肓计划(JB-ZR1135);2011年福建省新世纪优秀人才支持计划

摘　　要：ARMA模型在管理科学领域有着广泛的应用,组合预测可以提高ARMA模型的预测效果,但是如何选择最优模型组是十分重要但尚未解决的问题。本文提出了一个基于Kullback-Leibler信息量(简称K-L信息量)的最优模型组选择方法确定那些与最优模型无显著差异的模型形成最优模型组。最后,本文通过模拟数据比较了基于最优模型组的组合预测与根据AIC准则确定的单个最优模型的预测效果,组合预测效果要优于单模型预测。ARMA models are widely used in the field of management science.Combined forecasting can impove the effect of forecasting.However,how to select the best model group is very important but not well done.In this paper we propose a best model group selection method based on the Kullback-Leibler（K-L） information.First we measure the so called K-L distances between every candidate model and the true model using the K-L information,and then derive the confidence intervals of the gap between the K-L distence of each candidate model and the best model using the central limitation theory.Furthermore based on the confidence intervals we identify a group of models,which are not different significantly with the best model,as the best model group.Finally we compare the forecast ability of the best model group and the best model.The results show that the proposed method can improve the forecast with high probability when the best model is not the true model.

关 键 词：Kullback-Leibler信息量 最优模型组 组合预测 

分 类 号：O212[理学—概率论与数理统计]