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Estimating latent group structure in time-varying coefficient panel data models

机译：估计时变系数面板数据模型中的潜在群结构

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This paper studies the estimation of latent group structures in heterogeneous time-varying coefficient panel data models. While allowing the coefficient functions to vary over cross-sections provides a good way to model cross-sectional heterogeneity, it reduces the degree of freedom and leads to poor estimation accuracy when the time-series length is short. On the other hand, in a lot of empirical studies, it is not uncommon to find that heterogeneous coefficients exhibit group structures where coefficients belonging to the same group are similar or identical. This paper aims to provide an easy and straightforward approach for estimating the underlying latent groups. This approach is based on the hierarchical agglomerative clustering (HAC) of kernel estimates of the heterogeneous time-varying coefficients when the number of groups is known. We establish the consistency of this clustering method and also propose a generalised information criterion for estimating the number of groups when it is unknown. Simulation studies are carried out to examine the finite-sample properties of the proposed clustering method as well as the post-clustering estimation of the group-specific time-varying coefficients. The simulation results show that our methods give comparable performance to the penalised-sieve-estimation-based classifier-LASSO approach by Su et al. (2018), but are computationally easier. An application to a panel study of economic growth is also provided.
机译：本文研究了异构时变系数面板数据模型中潜在群结构的估计。虽然允许系数函数在横截面上变化提供了一种很好的建模横截面异质性的方法，但当时间序列长度较短时，它会降低自由度并导致较差的估计精度。另一方面，在许多实证研究中，发现异类系数表现出组结构的情况并不少见，其中属于同一组的系数相似或相同。本文旨在提供一种简单直接的方法来估计潜在的潜在群体。当已知组数时，此方法基于异构时变系数的内核估计的层次聚集聚类（HAC）。我们建立了这种聚类方法的一致性，并且提出了一种通用的信息准则，用于在未知时估计组数。进行了仿真研究，以检验所提出的聚类方法的有限样本属性以及特定于组的时变系数的聚类后估计。仿真结果表明，我们的方法与Su等人的基于惩罚筛估计的分类器LASSO方法具有可比的性能。 （2018），但计算起来更容易。还提供了对经济增长的专题研究的应用。

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