丁飞鹏. 固定效应部分线性单指标面板模型的惩罚经验似然估计[J]. 应用概率统计, 2019, 35(6): 573-593. DOI: 10.3969/j.issn.1001-4268.2019.06.003
引用本文: 丁飞鹏. 固定效应部分线性单指标面板模型的惩罚经验似然估计[J]. 应用概率统计, 2019, 35(6): 573-593. DOI: 10.3969/j.issn.1001-4268.2019.06.003
DING Feipeng. Penalized Empirical Likelihood Estimation for Partially Linear Single Index Panel Model with Fixed Effects[J]. Chinese Journal of Applied Probability and Statistics, 2019, 35(6): 573-593. DOI: 10.3969/j.issn.1001-4268.2019.06.003
Citation: DING Feipeng. Penalized Empirical Likelihood Estimation for Partially Linear Single Index Panel Model with Fixed Effects[J]. Chinese Journal of Applied Probability and Statistics, 2019, 35(6): 573-593. DOI: 10.3969/j.issn.1001-4268.2019.06.003

固定效应部分线性单指标面板模型的惩罚经验似然估计

Penalized Empirical Likelihood Estimation for Partially Linear Single Index Panel Model with Fixed Effects

  • 摘要: 结合二次推断函数法、滤子法和经验似然估计法,为个体内存在相关性的部分线性单指标固定效应面板模型建立了惩罚经验似然估计法.在一些正则条件下, 推导了模型估计量的大样本性质,证明了所提出的经验似然比渐近于卡方分布. 进一步,用~Monte Carlo~模拟和真实数据分析评价了估计方法在有限样本下的表现.

     

    Abstract: This paper constructs a penalized empirical likelihood estimation method via quadratic inference function method, filter method and empirical likelihood estimation method. Under some regular conditions, we derived the large sample properties of estimators and show that the proposed empirical likelihood ratio is asymptotically to chi-square distribution. Furthermore, the infinite sample performance of the proposed method is evaluated by Monte Carlo simulation and real data analysis.

     

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