Jackknife and Analytical Bias Reduction for Nonlinear Panel Models
针对非线性面板模型中固定效应估计因附带参数问题产生的严重偏差，本文提出使用面板刀切法或基于大T的解析偏差校正来降低偏差，并证明在T与n同速增长时刀切法能给出正确的渐近置信区间。
Fixed effects estimators of panel models can be severely biased because of the well-known incidental parameters problem. We show that this bias can be reduced by using a panel jackknife or an analytical bias correction motivated by large T. We give bias corrections for averages over the fixed effects, as well as model parameters. We find large bias reductions from using these approaches in examples. We consider asymptotics where T grows with n, as an approximation to the properties of the estimators in econometric applications. We show that if T grows at the same rate as n, the fixed effects estimator is asymptotically biased, so that asymptotic confidence intervals are incorrect, but that they are correct for the panel jackknife. We show T growing faster than n-super-1/3 suffices for correctness of the analytic correction, a property we also conjecture for the jackknife. Copyright The Econometric Society 2004.