论文标题
有效的横截面和面板数据校正
Efficient Bias Correction for Cross-section and Panel Data
论文作者
论文摘要
偏差校正通常可以改善估计器的有限样本性能。我们表明，只要偏差的估计值是渐近线性的，偏置校正方法的选择对半合理有效的参数估计器的高阶方差没有影响。还表明，对于具有标准形式的高阶扩展的估计器，引导程序，折刀和分析偏置估计值是渐近线性的。特别是，我们发现，对于各种估计量，直接引导偏置校正具有与更复杂的分析或折刀偏置更正相同的高阶差异。相比之下，不会以参数速率估算偏差的偏差校正，例如分式折刀，从而在I.I.D中导致较大的高阶方差。我们专注于设置。对于具有单个固定效果的横截面MLE和面板模型，我们表明分式样本夹克刀的较高差异项的术语是“剩下的”夹克刀的两倍。
Bias correction can often improve the finite sample performance of estimators. We show that the choice of bias correction method has no effect on the higher-order variance of semiparametrically efficient parametric estimators, so long as the estimate of the bias is asymptotically linear. It is also shown that bootstrap, jackknife, and analytical bias estimates are asymptotically linear for estimators with higher-order expansions of a standard form. In particular, we find that for a variety of estimators the straightforward bootstrap bias correction gives the same higher-order variance as more complicated analytical or jackknife bias corrections. In contrast, bias corrections that do not estimate the bias at the parametric rate, such as the split-sample jackknife, result in larger higher-order variances in the i.i.d. setting we focus on. For both a cross-sectional MLE and a panel model with individual fixed effects, we show that the split-sample jackknife has a higher-order variance term that is twice as large as that of the `leave-one-out' jackknife.
