AI 中文总结
本文针对带交互固定效应的高维部分线性面板模型提出panel DML-IFE方法，通过结合投影去因子化、奈曼正交得分与交叉拟合，经模拟和实证验证其优于传统IFE，可处理高维非线性协变量效应并修正偏差。
AI 中文摘要
因子结构是经济学和金融实证研究的核心，通常通过交互固定效应（Interactive Fixed Effects, IFE）对随时间变化的未观测异质性进行建模。现有IFE估计量依赖于协变量的低维线性设定，在使用包含未知函数形式控制变量的丰富数据集的应用中，这些假设的限制性越来越强。本文针对带有交互固定效应的高维部分线性面板模型，提出了双重机器学习估计量（panel DML-IFE）。该方法结合了基于投影的数据去因子化（遵循共同相关效应（Common Correlated Effects, CCE）的思路）、奈曼正交得分函数与交叉拟合程序，可同时处理结果与处理中的低秩因子结构，以及由机器学习算法估计的高维、可能非线性的协变量效应。蒙特卡洛模拟显示，在正确设定的线性情形之外，panel DML-IFE的性能优于传统IFE估计量，其偏差减少主要由时间维度和协变量维度驱动。对美国股票收益率的实证应用表明，当同时考虑高维非线性混淆与IFE的存在时，线性设定下记录的若干效应失去了统计显著性。
英文摘要
Factor structures are central to empirical work in economics and finance, and are usually used to model time-varying unobserved heterogeneity through interactive fixed effects (IFE). Existing IFE estimators rest on low-dimensional and linear specifications in the covariates, assumptions which are increasingly restrictive in applications drawing on rich datasets with controls of unknown functional form. This paper develops a Double Machine Learning estimator for the high-dimensional partially linear panel model with interactive fixed effects (panel DML-IFE). The method combines projection-based defactorisation of the data, in the spirit of Common Correlated Effects (CCE), with a Neyman-orthogonal score function and cross-fitting procedure, and accommodates low-rank factor structures in outcomes and treatments alongside high-dimensional, potentially nonlinear covariate effects estimated by machine learning algorithms. Monte Carlo simulations show that panel DML-IFE outperforms conventional IFE estimator outside the correctly-specified linear case, with bias reduction driven primarily by the time and covariate dimensions. An empirical application to U.S. stock returns shows that several effects documented under linear specifications lose statistical significance once high-dimensional nonlinear confounding and the presence of IFE are jointly accounted for.
