论文标题
具有固定效果的面板的强大估计和推断
Robust Estimation and Inference in Panels with Interactive Fixed Effects
论文作者
论文摘要
我们考虑在具有固定效果（即具有因子结构）的面板中对回归系数的估计和推断。我们证明，当某些因素较弱时，现有的估计器和置信区间（CI）可能会严重偏见和大小。我们提出的估计量提高了收敛速率和偏置感知的CI，无论因素强度如何，这些估计量保持有效。我们的方法将最小值线性估计的理论采用了核标准，该理论使用核定标准构成了核标准，该核定标准绑定在互动固定效应的初始估计值的误差上。我们产生的偏见感知的CI考虑了因素弱引起的剩余偏见。蒙特卡洛实验表明，当因素较弱时，对常规方法的实质性改善，当因素强时，估计准确性的成本最低。
We consider estimation and inference for a regression coefficient in panels with interactive fixed effects (i.e., with a factor structure). We demonstrate that existing estimators and confidence intervals (CIs) can be heavily biased and size-distorted when some of the factors are weak. We propose estimators with improved rates of convergence and bias-aware CIs that remain valid uniformly, regardless of factor strength. Our approach applies the theory of minimax linear estimation to form a debiased estimate, using a nuclear norm bound on the error of an initial estimate of the interactive fixed effects. Our resulting bias-aware CIs take into account the remaining bias caused by weak factors. Monte Carlo experiments show substantial improvements over conventional methods when factors are weak, with minimal costs to estimation accuracy when factors are strong.
