标题： 贝叶斯分析使用哈密顿蒙特卡罗的灵活 Heckman 选择模型 显示英文标题

标题： Bayesian analysis of flexible Heckman selection models using Hamiltonian Monte Carlo

摘要： 海克曼选择模型在计量经济学分析和其他社会科学中被广泛用于解决数据建模中的样本选择偏差问题。 海克曼选择模型的一个常见假设是误差项服从独立的双变量正态分布。 然而，现实世界的数据常常偏离这一假设，表现出重尾行为，如果不正确处理，可能导致不一致的估计。 在本文中，我们提出了一种海克曼选择模型的贝叶斯分析，该分析将高斯假设替换为正态分布的尺度混合类中的知名成员，例如学生t分布和污染正态分布。 对于这些复杂结构，使用Stan的默认No-U-Turn采样器来获得后验模拟。 通过大量的模拟研究，我们比较了具有正态分布、学生t分布和污染正态分布的海克曼选择模型的性能。 我们还通过将其应用于医疗保健和劳动力供给数据，展示了该方法的广泛应用性。 所提出的算法在R包HeckmanStan中实现。 显示英文摘要

摘要： The Heckman selection model is widely used in econometric analysis and other social sciences to address sample selection bias in data modeling. A common assumption in Heckman selection models is that the error terms follow an independent bivariate normal distribution. However, real-world data often deviates from this assumption, exhibiting heavy-tailed behavior, which can lead to inconsistent estimates if not properly addressed. In this paper, we propose a Bayesian analysis of Heckman selection models that replace the Gaussian assumption with well-known members of the class of scale mixture of normal distributions, such as the Student's-t and contaminated normal distributions. For these complex structures, Stan's default No-U-Turn sampler is utilized to obtain posterior simulations. Through extensive simulation studies, we compare the performance of the Heckman selection models with normal, Student's-t and contaminated normal distributions. We also demonstrate the broad applicability of this methodology by applying it to medical care and labor supply data. The proposed algorithms are implemented in the R package HeckmanStan.