Dummy Endogenous Variables in Threshold Crossing Models, with Applications to Health Economics

阈值交叉模型中的虚拟内生变量及其在健康经济学中的应用

• 批准号: 0832845
• 负责人: Edward Vytlacil
• 金额: $ 5.38万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-02-28 至 2011-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0832845&HistoricalAwards=false
• 关键词: Dummy; Endogenous; Variables; Threshold; Crossing

Threshold crossing models are a standard framework for analyzing binary data in microeconomics, medicine, and elsewhere. For example, analysts often model mortality by imagining latent health status falling below a threshold. A common problem arises when one of the regressors is a dummy endogenous variable, such as a medical procedure that is performed on individuals with the worst unobserved health. Standard methodologies for recovering the average effect of the endogenous variable either depend on the validity of parametric distributional assumptions or require strong properties on an instrument. Instrumental variables analysis recovers the 'Local Average Treatment Effect' but not the average effect.The PIs propose developing a new non-parametric methodology for evaluating the average effect of a dummy endogenous variable in a threshold crossing model where there is an instrument. Using an early version of this methodology, the PI's have constructed sharp bounds. When all regressors are discrete, the PI's have developed confidence sets that asymptotically contain each point in the bounds with a given specified probability. The PI's will extend their procedures for inference, including to allow for continuous regressors and to consider confidence sets that asymptotically contain the entire identified set with the desired probability. The PI's will develop bounds for other classes of threshold crossing models and limited dependent variable models beyond binary choice models, for example, ordered choice models. The PI's will extend their analysis to consider dummy endogenous regressors in discrete-time, dynamic outcome models.The analysis is applied to an important medical problem. The placement of Swan-Ganz catheters is an extremely common procedure with over 2 million patients in North America catheterized each year. Doctors debate whether the greater observed mortality of patients receiving Swan-Ganz catheterization can be attributed to catheterization itself, or are due to the unobserved worse health of catheterized patients. In preliminary analysis, the PI's have considered the effect of Swan-Ganz catheterization on later mortality. The proposed theoretical work will allow the PI's to apply statistical inference procedures to account for the dynamic nature of the outcome variable to this situation.The project is of broad intellectual significance because it advances the theoretical literature on endogenous variables in nonparametric, nonseparable models. Previous approaches require a continuous endogenous regressor, a continuous outcome variable, or particularly strong statistical identification requirements. The research introduces a new approach without the need for any of these objects, which are often not available in empirical settings.This research program will also have broader impacts outside of theoretical econometrics. The empirical project proposed is important in its own right, and the results will provide doctors with guidance on thorny patient care decisions. The analysis of the effect of binary endogenous variables on limited dependent variables is a common problem in empirical economics, sociology, political science, and medicine; hence, the theoretical work will have wide applicablity.

阈值交叉模型是用于分析微观经济学、医学和其他领域的二进制数据的标准框架。例如，分析师通常通过想象潜在健康状况低于阈值来模拟死亡率。当其中一个回归变量是虚拟内生变量时，会出现一个常见的问题，例如对健康状况最差的个人进行的医疗程序。恢复内生变量的平均效应的标准方法要么取决于参数分布假设的有效性，要么要求工具具有很强的属性。工具变量分析恢复了“局部平均治疗效应”，但没有恢复平均效应。PI建议开发一种新的非参数方法，用于评估存在工具的阈值交叉模型中虚拟内生变量的平均效应。使用这种方法的早期版本，PI已经构建了尖锐的界限。当所有回归量都是离散的时，PI已经开发了置信集，该置信集渐进地包含具有给定指定概率的边界中的每个点。PI将扩展其推理程序，包括允许连续回归量，并考虑渐近包含具有所需概率的整个已识别集的置信集。PI将为其他类别的阈值交叉模型和二元选择模型之外的有限因变量模型（例如，有序选择模型）制定界限。PI的将扩展他们的分析，以考虑虚拟内生回归离散时间，动态结果模型。分析应用于一个重要的医疗问题。Swan-Ganz导管的放置是一种非常常见的手术，在北美每年有超过200万患者接受导管插入术。医生们争论接受Swan-Ganz导管插入术的患者观察到的更高死亡率是否可以归因于导管插入术本身，或者是由于未观察到的导管插入患者的健康状况恶化。在初步分析中，PI考虑了Swan-Ganz导管插入术对后期死亡率的影响。拟议的理论工作将允许PI的应用统计推断程序来解释结果变量的动态性质，以这种situation.The项目是广泛的智力意义，因为它推进了非参数，不可分离模型的内生变量的理论文献。以前的方法需要一个连续的内生回归变量，一个连续的结果变量，或特别强的统计识别要求。这项研究引入了一种新的方法，而不需要任何这些对象，这往往是不存在的经验设置。这一研究计划也将有更广泛的影响以外的理论计量经济学。提出的实证项目本身就很重要，其结果将为医生提供棘手的病人护理决策的指导。分析二元内生变量对有限因变量的影响是实证经济学、社会学、政治学和医学中的一个共同问题，因此，这一理论工作具有广泛的适用性。