面板不平衡时固定效应估计量的推断
Inference for Fixed Effects Estimators when Panels are Unbalanced
AI总结：
研究面板不平衡时固定效应估计量的推断，在特定渐近框架下推导其渐近性质，针对未校正估计量的偏差问题，提出无需知晓预定变量的去偏估计量来处理两种偏差来源。
AI中文摘要：
我们在横截面单位数量和时期数量共同增长的渐近框架中，推导了存在缺失观测值的双向固定效应M估计量的渐近性质。我们允许选择过程是确定性的（取决于未观测效应和初始条件）、随机的或混合的，并且仅施加条件均值限制。未校正的估计量渐近正态但不以零为中心，存在附带参数和反馈偏差。反馈偏差可由结果方程中的预定回归变量和预定选择过程引起。我们提出了去偏估计量，无需知道哪些回归变量或选择成分是预定的，就能处理这两种偏差来源。
英文摘要：
We develop the asymptotic theory for two-way fixed effects M-estimators in unbalanced panels, within a framework where both panel dimensions grow large at proportional rates. The selection process may be deterministic, stochastic, or a combination of the two. We require neither a missing-at-random condition nor a selection equation, only a conditional mean restriction on the outcome. The uncorrected estimators are asymptotically normal but not correctly centered due to incidental parameter bias and feedback bias. The latter arises when regressors or the selection indicator respond to past outcomes. We propose debiased estimators that remove both biases without requiring knowledge of which regressors or selection components are predetermined. Simulations show that the corrections remove most of the bias and restore coverage close to nominal levels. Revisiting a study on capital inflow surges and banking crises, we find that the corrections leave qualitative conclusions unchanged but substantially shift the estimated magnitudes.
