Identifying Heterogeneity in Dynamic Panel Models with Individual Parameter Contribution Regression
Identifying Heterogeneity in Dynamic Panel Models with Individual Parameter Contribution Regression
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DOI：
10.1080/10705511.2019.1667240
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发表时间：
2019-11-04
影响因子：
6
通讯作者：
Voelkle, Manuel C.
中科院分区：
文献类型：
--
作者：
Arnold, Manuel;Oberski, Daniel L.;Voelkle, Manuel C.
动态面板模型是研究重复测量变量之间相互关系的流行方法。通常，动态面板模型是在结构方程建模(SEM)框架内指定和估计的。威胁此类模型有效性的一个地方性问题是未经建模的异质性。最近，个体参数贡献(IPC)回归被提出为一种灵活的方法来研究扫描电子显微镜参数作为观测协变量的函数的异质性。在本文中，我们推导了如何计算IPC来进行一般最大似然估计，并评估了IPC回归在离散和连续时间估计动态面板模型中的组差异的性能。我们表明，IPC回归在具有较大分组差异的样本中可能会有轻微的偏差，并提出了一种偏差校正程序。IPC回归显示，对于离散时间模型，总体上是有希望的结果。然而，由于高度非线性的参数约束，在将IPC回归应用于连续时间模型时应谨慎。
Dynamic panel models are a popular approach to study interrelationships between repeatedly measured variables. Often, dynamic panel models are specified and estimated within a structural equation modeling (SEM) framework. An endemic problem threatening the validity of such models is unmodelled heterogeneity. Recently, individual parameter contribution (IPC) regression was proposed as a flexible method to study heterogeneity in SEM parameters as a function of observed covariates. In the present paper, we derive how IPCs can be calculated for general maximum likelihood estimates and evaluate the performance of IPC regression to estimate group differences in dynamic panel models in discrete and continuous time. We show that IPC regression can be slightly biased in samples with large group differences and present a bias correction procedure. IPC regression showed generally promising results for discrete time models. However, due to highly nonlinear parameter constraints, caution is indicated when applying IPC regression to continuous time models.