Inference in high-dimensional two-way fixed effects panel data models
研究了带个体和时间固定效应的高维工具变量面板数据模型，提出先用双重去均值去除固定效应，再用Lasso或Cluster-Lasso选择最优工具变量并进行两阶段最小二乘估计，证明了估计量的相合性和渐近正态性。
This paper studies a triangular simultaneous panel-data model with individual and time fixed effects and a high-dimensional set of instruments in the first stage. The endogenous regressor is driven by an unknown function of observable instruments and two sets of fixed effects, while the structural equation contains the same two-way fixed effects. We propose a two-step procedure: first, we remove both individual and time effects by a double-demeaning transformation; second, we estimate an optimal instrument for the endogenous regressor using Lasso or Cluster-Lasso and then apply two-stage least squares (2SLS). Under approximate sparsity and high-level regularity conditions, we show that the resulting 2SLS estimator is N T -consistent and asymptotically normal. The proofs adapt the panel Cluster-Lasso results of Belloni et al. (2016) to the two-way fixed effects transformation. • Two-way fixed effects panel model with high-dimensional instruments estimated via Lasso. • Double-demeaning removes individual and time effects before Lasso instrument selection. • Cluster-Lasso first stage yields a root-NT consistent and asymptotically normal estimator. • Time fixed effects add no asymptotic cost over the one-way fixed effects case. • Cross-validated penalty outperforms fixed tuning in finite-sample simulations.