Estimating latent group structure in time-varying coefficient panel data models
时变系数面板模型中的潜在分组结构估计AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究异质时变系数面板的潜在分组，并明确提供经济增长面板应用。
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时变系数面板模型中的潜在分组结构估计
- 研究问题
- 当面板个体的时变系数具有未知分组结构时，如何识别组别并估计组数，以缓解短时间维度下的估计精度问题？
- 方法与思路
- 作者对个体时变系数的核估计进行层次凝聚聚类，证明已知组数时的聚类一致性，并用广义信息准则估计未知组数。
- 材料陈述的贡献
- 摘要称方法在模拟中与 classifier-LASSO 表现相当而计算更简单，并考察分组后系数估计及经济增长面板应用。
- 阅读关注点
- 应核对组间分离、核估计与面板维度的条件，信息准则如何选择组数，以及计算比较和分组后估计的评价方式。
- 阅读边界
- 摘要未给出一致性条件、组数准则公式或计算成本数值；性能相当且计算较易的陈述限于所报告的比较设计。
原文摘要
Summary 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.