Difference-based estimation and model identification for panel data semiparametric models with cross-section dependence

Difference-based estimation and model identification for panel data semiparametric models with cross-section dependence
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具有截面依赖性的面板数据半参数模型的基于差异的估计和模型识别

DOI:
10.1080/03610926.2013.857417
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发表时间:
2016-02
期刊:
Communications in Statistics - Theory and Methods
影响因子:
--
通讯作者:
Li Rui
Li Rui
中科院分区:
其他
文献类型:
--
作者:
Zhao Haibing;Li Rui

文献摘要

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摘要本文考虑了具有固定效应和非参数时间趋势函数的面板数据部分线性回归模型。数据可以通过线性回归和误差分量进行相关的交叉个体。与使用非参数平滑技术的方法不同,提出了一种基于差分的方法来估计模型的线性回归系数,以避免带宽选择。本文利用差分技术完全消除了非参数函数对线性回归系数估计的影响,而不是固定效应。因此,期望得到一种更有效的参数部分估计器,仿真结果表明这是正确的。对于非参数元件,采用多项式样条技术。给出了参数部分和非参数部分估计量的渐近性质。我们还展示了如何从线性部分的协变量中选择信息,通过使用基于差分的最小二乘目标函数上的平滑裁剪绝对偏差惩罚估计,并且所得到的估计在选择正确的模型方面表现出渐近性以及Oracle过程。
Abstract In this article, we consider a panel data partially linear regression model with fixed effect and non parametric time trend function. The data can be dependent cross individuals through linear regressor and error components. Unlike the methods using non parametric smoothing technique, a difference-based method is proposed to estimate linear regression coefficients of the model to avoid bandwidth selection. Here the difference technique is employed to eliminate the non parametric function effect, not the fixed effects, on linear regressor coefficient estimation totally. Therefore, a more efficient estimator for parametric part is anticipated, which is shown to be true by the simulation results. For the non parametric component, the polynomial spline technique is implemented. The asymptotic properties of estimators for parametric and non parametric parts are presented. We also show how to select informative ones from a number of covariates in the linear part by using smoothly clipped absolute deviation-penalized estimators on a difference-based least-squares objective function, and the resulting estimators perform asymptotically as well as the oracle procedure in terms of selecting the correct model.
DOI: 10.2139/ssrn.1677767
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影响因子: --
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