A Reduced Rank Regression Approach to Coincident and Leading Indexes Building*
提出用降秩回归框架构建一致指数和先行指数，基于先行指数差分对一致指数差分的最优线性预测，利用多项式序列相关共同特征构建复合变量，并用美国商业周期指标进行实证。
Abstract This paper proposes a reduced rank regression framework for constructing a coincident index (CI) and a leading index (LI). Based on a formal definition that requires that the first differences of the LI are the best linear predictor of the first differences of the CI, it is shown that the notion of polynomial serial correlation common features can be used to build these composite variables. Concepts and methods are illustrated by an empirical investigation of the US business cycle indicators.