通过选和排名算法对线性面板数据模型中常见断裂的估计
Fuxiao Li1, Yanting Xiao2, Zhanshou Chen3,4
1Department of Applied Mathematics, Xi'an University of Technology, Xi'an, 710054, China. fx_lmzq@163.com.
Scientific reports
|April 2, 2025
概括
本研究介绍了一种选和排名算法,用于检测面板数据模型中的结构断裂. 该方法有效地确定了共同的断点,增强了经济增长分析.
科学领域:
- 计量经济学 计量经济学
- 时间序列分析时间序列分析
- 经济建模经济建模
背景情况:
- 面板数据模型对于分析跨多个实体的经济现象至关重要.
- 识别这些模型中的结构性缺陷对于准确的政策评估和预测至关重要.
- 现有的方法可能面临的挑战是准确估计在静态和动态面板数据中共同的断点.
研究的目的:
- 开发和验证一种新的选和排名算法,用于估计线性面板数据模型中的常见断点.
- 评估拟议的算法在静态和动态面板数据设置中的性能.
- 将算法应用于现实世界经济数据集,以识别重大结构变化.
主要方法:
- 估计回归系数使用协差估计的静态模型和动态模型的时刻的概括方法.
- 一个多阶段的选和排名算法,涉及当地统计,门规则和信息标准,以确定断点.
- 蒙特卡洛模拟以评估拟议方法的有限样本性能.
主要成果:
- 拟议的选和排名算法在各种面板数据模型规格的有限样本中显示出良好的性能.
- 该算法成功地确定了农村消费需求与中国经济增长之间的关系中的一个重要断点.
- 该应用程序强调了该方法在揭示经济关系中的结构变化方面的实际实用性.
结论:
- 开发的选和排名算法提供了一个强大的和有效的方法来估计线性面板数据模型中常见的断点.
- 该方法在静态和动态设置中都可靠,为计量经济学家提供了有价值的工具.
- 该研究强调了在分析经济增长的驱动因素时考虑结构性断裂的重要性,正如中国案例研究所示.
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