Selecting the regularization parameters in high-dimensional panel data models: Consistency and efficiency
研究了存在大量潜在预测变量和不可观测共同因子的面板数据模型，提出一种面板信息准则来选择正则化参数和共同因子数量，该准则在模型正确设定时能一致识别真实模型，在模型误设时能实现渐近有效的模型选择。
This article considers panel data models in the presence of a large number of potential predictors and unobservable common factors. The model is estimated by the regularization method together with the principal components procedure. We propose a panel information criterion for selecting the regularization parameter and the number of common factors under a diverging number of predictors. Under the correct model specification, we show that the proposed criterion consistently identifies the true model. If the model is instead misspecified, the proposed criterion achieves asymptotically efficient model selection. Simulation results confirm these theoretical arguments.