Jackknife and analytical bias reduction for nonlinear panel models
Jackknife and analytical bias reduction for nonlinear panel models
复制标题
DOI：
10.1111/j.1468-0262.2004.00533.x
复制
发表时间：
2004-07-01
期刊：
影响因子：
6.1
通讯作者：
Newey, W
中科院分区：
文献类型：
--
作者：
Hahn, J;Newey, W
由于众所周知的附带参数问题，面板模型的固定效应估计可能会出现严重的偏差。我们证明，这种偏差可以通过使用面板折刀或大T激励的解析偏差校正来减少。我们给出了固定效应的平均值以及模型参数的偏差校正。在实例中，我们发现使用这些方法可以大大减少偏差。我们考虑T随n增长的渐近性，作为计量经济学应用中估计量的性质的近似。我们证明了如果T与n以相同的速度增长，固定效应估计是渐近有偏的，因此渐近可信区间是不正确的，但它们对于面板折叠式是正确的。我们证明了T的增长快于n(1/3)就足以证明解析修正的正确性，这也是我们对折刀的一个猜想。
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(1/3) suffices for correctness of the analytic correction, a property we also conjecture for the jackknife.