Latent Group Structure in Linear Panel Data Models with Endogenous Regressors
Latent Group Structure in Linear Panel Data Models with Endogenous Regressors
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DOI：
10.2139/ssrn.4825450
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发表时间：
2024-05
期刊：
影响因子：
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通讯作者：
Junho Choi;Ryo Okui
中科院分区：
文献类型：
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作者：
Junho Choi;Ryo Okui
本文研究具有内生回归变量和潜在群体结构系数的线性面板数据模型的估计问题。我们考虑了群体特有系数向量的工具变量估计。我们证明了将KMeans算法直接应用于广义矩目标函数方法并不能得到唯一的估计。我们新发展并在理论上证明了两阶段估计方法的合理性，这些方法应用KMeans算法来回归因变量与内生回归变量的预测值。蒙特卡罗模拟的结果表明，即使真正的第一阶段回归是完全异质的，使用潜在基团结构建模的第一阶段的两阶段估计也获得了良好的分类精度。我们应用我们的估计方法来重新审视收入和民主之间的关系。
This paper concerns the estimation of linear panel data models with endogenous regressors and a latent group structure in the coefficients. We consider instrumental variables estimation of the group-specific coefficient vector. We show that direct application of the Kmeans algorithm to the generalized method of moments objective function does not yield unique estimates. We newly develop and theoretically justify two-stage estimation methods that apply the Kmeans algorithm to a regression of the dependent variable on predicted values of the endogenous regressors. The results of Monte Carlo simulations demonstrate that two-stage estimation with the first stage modeled using a latent group structure achieves good classification accuracy, even if the true first-stage regression is fully heterogeneous. We apply our estimation methods to revisiting the relationship between income and democracy.