Data-driven model selection within the matrix completion method for causal panel data models
因果面板模型矩阵补全方法中的数据驱动模型选择
Matrix completion estimators are employed in causal panel data models to regulate the rank of the underlying factor model using nuclear norm minimization. This convex optimization problem enables concurrent regularization of a potentially high-dimensional set of covariates to shrink the model size. For valid finite sample inference, we adopt a permutation-based approach and prove its validity for any treatment assignment mechanism. Simulations illustrate the consistency of…