This paper studies the Bridge estimator for a high-dimensional panel data model with heterogeneous varying coefficients, where the random errors are assumed to be serially correlated and cross-sectionally dependent. We establish oracle efficiency and the asymptotic distribution of the Bridge estimator, when the number of covariates increases to infinity with the sample size in both dimensions. A BIC-type criterion is also provided for tuning parameter selection. We further generalise the marginal Bridge estimator for our model to asymptotically correctly identify the covariates with zero coefficients even when the number of covariates is greater than the sample size under a partial orthogonality condition. The finite sample performance of the proposed estimator is demonstrated by simulated data examples, and an empirical application with the US stock dataset is also provided.

中文翻译：

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具有横截面依赖性的异构面板数据模型中的变量选择

本文研究了具有异质变系数的高维面板数据模型的 Bridge 估计器，其中假设随机误差是序列相关和横截面相关的。当协变量的数量随着两个维度的样本量增加到无穷大时，我们建立了 Oracle 效率和 Bridge 估计量的渐近分布。还提供了一个 BIC 类型的标准来调整参数选择。我们进一步推广模型的边际 Bridge 估计量，以渐进地正确识别系数为零的协变量，即使在部分正交条件下协变量的数量大于样本量。所提出的估计器的有限样本性能由模拟数据示例证明，