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英文文献：Oracle Efficient Variable Selection in Random and Fixed Effects Panel Data Models-Oracle在随机和固定效应面板数据模型中的有效变量选择
英文文献作者：Anders Bredahl Kock
英文文献摘要：
This paper generalizes the results for the Bridge estimator of Huang et al. (2008) to linear random and fixed effects panel data models which are allowed to grow in both dimensions. In particular, we show that the Bridge estimator is oracle efficient. It can correctly distinguish between relevant and irrelevant variables and the asymptotic distribution of the estimators of the coefficients of the relevant variables is the same as if only these had been included in the model, i.e. as if an oracle had revealed the true model prior to estimation. In the case of more explanatory variables than observations we prove that the Marginal Bridge estimator can asymptotically correctly distinguish between relevant and irrelevant explanatory variables. We do this without assuming sub-Gaussianity of the error terms. However, a partial orthogonality condition of the same type as in Huang et al. (2008) is needed.
本文将Huang等人(2008)的桥梁估计结果推广到允许在两个维度上都增长的线性随机和固定效应面板数据模型。特别地，我们证明了桥估计器是oracle有效的。它能正确区分相关变量和不相关变量，相关变量系数的估计量的渐近分布与模型中只包含这些估计量的估计量是一样的，即在估计之前，先知已经揭示了真实的模型。在解释变量多于观测值的情况下，我们证明了边际桥估计量可以渐近正确地区分相关和不相关的解释变量。我们这样做时没有假设误差项的亚高斯性。然而，需要一个与Huang et al.(2008)相同类型的部分正交条件。
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