Semiparametric trending panel data models with cross-sectional dependence
Semiparametric trending panel data models with cross-sectional dependence
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DOI：
10.1016/j.jeconom.2012.07.001
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发表时间：
2012-11-01
影响因子：
6.3
通讯作者：
Li, Degui
中科院分区：
文献类型：
--
作者：
Chen, Jia;Gao, Jiti;Li, Degui
为了描述面板数据分析中的非线性趋势现象，引入了一个半参数固定效应模型，该模型考虑了回归变量和残差的横截面依赖性。提出了一种基于第一阶段局部线性拟合的混合半参数轮廓似然虚拟变量方法来估计参数向量和非线性时间趋势函数。由于时间序列长度T和横截面尺寸N都趋于无穷大，因此参数向量的估计量是渐近正态的，具有根（NT）收敛速度。同时，也建立了趋势函数的非参数估计的渐近分布，其根（NTh）收敛速度。两个仿真例子说明了所提出的方法的有限样本性能。此外，所提出的模型和估计方法适用于CPI数据集以及输入输出数据集。(C)2012爱思唯尔有限公司版权所有。
A semiparametric fixed effects model is introduced to describe the nonlinear trending phenomenon in panel data analysis and it allows for the cross-sectional dependence in both the regressors and the residuals. A pooled semiparametric profile likelihood dummy variable approach based on the first-stage local linear fitting is developed to estimate both the parameter vector and the nonlinear time trend function. As both the time series length T and the cross-sectional size N tend to infinity, the resulting estimator of the parameter vector is asymptotically normal with a root-(NT) convergence rate. Meanwhile, the asymptotic distribution for the nonparametric estimator of the trend function is also established with a root-(NTh) convergence rate. Two simulated examples are provided to illustrate the finite sample performance of the proposed method. In addition, the proposed model and estimation method are applied to a CPI data set as well as an input-output data set. (C) 2012 Elsevier B.V. All rights reserved.