具有变样本选择偏差和色散参数的广义Heckman模型
A Generalized Heckman Model With Varying Sample Selection Bias and Dispersion Parameters
摘要：
许多建议已经出现作为Heckman选择模型的替代方案，主要是为了解决其正常假设的非鲁棒性。2001年医疗支出跟踪调查的数据经常被用来说明Heckman模型的不稳健性。在本文中，我们提出了一个推广的Heckman样本选择模型，允许样本选择偏差和分散参数依赖于协变量。我们表明，非鲁棒性的Heckman模型可能是由于假设的恒定样本选择偏差参数，而不是正态性假设。我们提出的方法使我们能够理解哪些协变量对于解释样本选择偏倚现象是重要的，而不仅仅是形成关于其存在的结论。我们探讨了我们提出的广义Heckman模型的极大似然估计（MLE）的推理方面。更具体地说，我们证明了这个模型满足一些正则条件，使它确保的MLE的一致性和渐近正态性。适当的评分残差样本选择模型，并解决模型的充分性。模拟结果检查有限样本的估计行为，并验证不考虑不同的样本选择偏差和分散参数的后果。我们表明，正常的假设分析医疗支出数据是合适的，使用我们的方法得出的结论是一致的，从以前的文献研究结果。此外，我们确定哪些协变量是相关的，以解释在这个重要的数据集样本选择偏差的存在。
英文摘要：
Many proposals have emerged as alternatives to the Heckman selection model, mainly to address the non-robustness of its normal assumption. The 2001 Medical Expenditure Panel Survey data is often used to illustrate this non-robustness of the Heckman model. In this paper, we propose a generalization of the Heckman sample selection model by allowing the sample selection bias and dispersion parameters to depend on covariates. We show that the non-robustness of the Heckman model may be due to the assumption of the constant sample selection bias parameter rather than the normality assumption. Our proposed methodology allows us to understand which covariates are important to explain the sample selection bias phenomenon rather than to only form conclusions about its presence. We explore the inferential aspects of the maximum likelihood estimators (MLEs) for our proposed generalized Heckman model. More specifically, we show that this model satisfies some regularity conditions such that it ensures consistency and asymptotic normality of the MLEs. Proper score residuals for sample selection models are provided, and model adequacy is addressed. Simulated results are presented to check the finite-sample behavior of the estimators and to verify the consequences of not considering varying sample selection bias and dispersion parameters. We show that the normal assumption for analyzing medical expenditure data is suitable and that the conclusions drawn using our approach are coherent with findings from prior literature. Moreover, we identify which covariates are relevant to explain the presence of sample selection bias in this important dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
