Abstract In this paper, we study the model detection and estimation for varying coefficient panel data models with fixed effects. We first propose a data transformation approach to eliminate fixed effects. Then, using the basis function approximations and the group SCAD penalty, we develop a combined penalization procedure to select the significant covariates, detect the true structure of the model, i.e., identify the nonzero constant coefficients and the varying coefficients, and estimate the unknown regression coefficients simultaneously. Under some mild conditions, we show that the proposed procedure can identify the true model structure consistently, and the penalized estimators have the oracle properties. At last, we illustrate the finite sample performance of the proposed methods with some simulation studies and a real data application.

中文翻译：

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固定效应变系数面板数据模型的模型检测与估计

摘要 在本文中，我们研究了具有固定效应的变系数面板数据模型的模型检测和估计。我们首先提出了一种数据转换方法来消除固定效应。然后，使用基函数近似和组 SCAD 惩罚，我们开发了一个组合惩罚程序来选择显着协变量，检测模型的真实结构，即识别非零常数系数和变化系数，并估计未知回归同时系数。在一些温和的条件下，我们表明所提出的程序可以一致地识别真实的模型结构，并且惩罚估计量具有预言机属性。最后，我们通过一些模拟研究和实际数据应用来说明所提出方法的有限样本性能。