CLUSTER-BASED REGULARIZED SLICED INVERSE REGRESSION FOR FORECASTING MACROECONOMIC VARIABLES

被引：0
|
作者：
YU Yue
CHEN Zhihong
YANG Jie
机构：
[1] TradeLink L.L.C.,71 S. Wacker Drive Suite 1900, Chicago, Illinois, USA
[2] School of International Trade and Economics, University of International Business and Economics
[3] Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago
来源：
系统科学与复杂性学报(英文版) | 2014年 / 01期
基金：
美国国家科学基金会;
关键词：
D O I：
暂无
中图分类号：
F113 [世界经济问题]; F224 [经济数学方法];
学科分类号：
020202 ; 020206 ; 030206 ; 0701 ; 070104 ; 1407 ;
摘要：
This paper concerns the dimension reduction in regression for large data set.The authors introduce a new method based on the sliced inverse regression approach,called cluster-based regularized sliced inverse regression.The proposed method not only keeps the merit of considering both response and predictors’information,but also enhances the capability of handling highly correlated variables.It is justified under certain linearity conditions.An empirical application on a macroeconomic data set shows that the proposed method has outperformed the dynamic factor model and other shrinkage methods.
引用
收藏
页码：75 / 91
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