基于部分验证的程序参与估计
Estimating Program Participation with Partial Validation
AI总结：
本文提出了一种利用部分验证数据克服二元选择模型中误分类偏差的方法，并通过实证分析展示了其在健康保险覆盖率研究中的应用。
摘要：
本文考虑在调查响应可能被误分类的情况下，二元选择模型的估计问题，其中其中一个响应类别可以被验证。当调查问题关于参与包含对该特定响应类别的后续问题时，部分验证可能会发生。在这种情况下，我们证明初始的双侧误分类问题可以基于部分验证的响应转换为单侧问题。使用更新的响应进行估计并不能解决或缓解误分类偏差，我们推导了在一般条件下由此产生的渐近偏差。随后，我们展示如何利用部分验证的响应来构建参与模型，并提出一致且渐近正态的估计量，以克服误分类误差。提供了蒙特卡洛模拟以展示所提出和选定现有方法的有限样本性能。我们还提供了加纳健康保险覆盖率决定因素的实证示例。我们讨论了对调查问卷设计的启示，使研究人员能够在不依赖相对昂贵且往往不完美的验证数据的情况下克服误分类偏差。
英文摘要：
This paper considers the estimation of binary choice models when survey responses are possibly misclassified but one of the response category can be validated. Partial validation may occur when survey questions about participation include follow-up questions on that particular response category. In this case, we show that the initial two-sided misclassification problem can be transformed into a one-sided one, based on the partially validated responses. Using the updated responses naively for estimation does not solve or mitigate the misclassification bias, and we derive the ensuing asymptotic bias under general conditions. We then show how the partially validated responses can be used to construct a model for participation and propose consistent and asymptotically normal estimators that overcome misclassification error. Monte Carlo simulations are provided to demonstrate the finite sample performance of the proposed and selected existing methods. We provide an empirical illustration on the determinants of health insurance coverage in Ghana. We discuss implications for the design of survey questionnaires that allow researchers to overcome misclassification biases without recourse to relatively costly and often imperfect validation data.
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。
