面板数据的函数系数量子回归与潜伏组结构的面板数据
Xiaorong Yang1, Jia Chen2, Degui Li3
1School of Statistics and Mathematics,Zhejiang Gongshang University.
概括
本研究引入了一种新的方法,通过识别潜伏组结构来估计复杂的面板定量回归模型. 这种方法简化了估计,并准确地揭示了各种量子级别的基础数据同质性.
科学领域:
- 计量经济学 计量经济学
- 统计建模 统计建模
- 定量分析 定量分析
背景情况:
- 面板定量回归模型对于分析具有个体效应的异质数据至关重要.
- 在这种情况下,对功能系数模型的估计存在挑战,原因是大型数据集的横截面和时间依赖性.
- 现有的方法往往难以有效地处理非参数函数系数的复杂性.
研究的目的:
- 开发一种可靠的方法来估计功能系数模型在面板定量回归与个体效应.
- 强加一个潜在的组结构,以减少非参数函数系数的数量.
- 准确估计特定组系数,并确定最佳的组数.
主要方法:
- 使用对特定学科的功能系数进行初步的局部线性定量估计.
- 使用经典的聚合集群算法来估计未知的组结构.
- 提出一个易于实施的比例标准来确定组号.
- 引入分组后的局部线性平滑方法,用于估计特定组系数.
主要成果:
- 一致地估计了集团的数量和结构.
- 对具有竞争性规范化率的估计器的非对称正常分布理论的推导.
- 通过模拟研究来证明方法的有效性.
- 在现实数据中,识别不同量子级别的不同同质性结构的变化.
结论:
- 拟议的方法有效地通过利用潜伏组结构来估计面板定量回归中的功能系数模型.
- 该方法简化了复杂的模型,并为组结构和系数提供了一致的估计.
- 这些发现通过模拟和实际应用得到了验证,突出了其在住房价格等各种数据集中的实用性.
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