基于交互效应的面板数据子空间聚类

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摘要翻译：
本文讨论了一个具有不可观测分组因子结构的面板数据的统计模型，该模型与回归子相关，且分组隶属度是未知的。假设因子负载在不同的子空间中，并考虑了因子负载的子空间聚类。提出了一种最小二乘子空间聚类估计(LSSC)方法，通过最小化最小二乘准则估计模型参数，同时进行子空间聚类。证明了该子空间聚类算法的相合性，并在一定条件下研究了估计过程的渐近性质。通过蒙特卡罗仿真研究，说明了该方法的优越性。进一步讨论了因子子空间个数、因子维数和子空间维数未知的情况。为了说明目的，在允许未观察到的因素和因素负荷的子空间模式的情况下，将所提出的方法用于研究各国收入与民主之间的联系。
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英文标题：
《Subspace Clustering for Panel Data with Interactive Effects》
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作者：
Jiangtao Duan, Wei Gao, Hao Qu, Hon Keung Tony
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最新提交年份：
2021
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  In this paper, a statistical model for panel data with unobservable grouped factor structures which are correlated with the regressors and the group membership can be unknown. The factor loadings are assumed to be in different subspaces and the subspace clustering for factor loadings are considered. A method called least squares subspace clustering estimate (LSSC) is proposed to estimate the model parameters by minimizing the least-square criterion and to perform the subspace clustering simultaneously. The consistency of the proposed subspace clustering is proved and the asymptotic properties of the estimation procedure are studied under certain conditions. A Monte Carlo simulation study is used to illustrate the advantages of the proposed method. Further considerations for the situations that the number of subspaces for factors, the dimension of factors and the dimension of subspaces are unknown are also discussed. For illustrative purposes, the proposed method is applied to study the linkage between income and democracy across countries while subspace patterns of unobserved factors and factor loadings are allowed.
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PDF链接：
https://arxiv.org/pdf/1909.09928

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关键词：交互效应 面板数据 econometrics Illustrative SIMULTANEOUS model method 研究 LSSC 情况