This paper develops a general framework for estimation of high-dimensional conditional factor models via nuclear norm regularization. We establish large sample properties of the estimators, and provide an efficient computing algorithm for finding the estimators as well as a cross validation procedure for choosing the regularization parameter. The general framework allows us to estimate a variety of conditional factor models in a unified way and quickly deliver new asymptotic results. We apply the method to analyze the cross section of individual US stock returns, and find that imposing homogeneity may improve the model's out-of-sample predictability.

翻译：本文为通过核规范规范化来估计高维有条件要素模型制定了一个总体框架。 我们建立了测量者的大量样本属性,并为寻找估算者提供了高效的计算算法,并为选择规范化参数提供了一个交叉验证程序。 总体框架使我们能够以统一的方式估算各种有条件要素模型,并迅速提供新的零星结果。 我们运用了分析单个美国股票回报跨部分的方法,发现强制同质性可能会改善模型的外表可预测性。