当前位置: X-MOL 学术Econom. J. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Partial identification in nonseparable count data instrumental variable models
The Econometrics Journal ( IF 1.9 ) Pub Date : 2019-12-20 , DOI: 10.1093/ectj/utz025
Dongwoo Kim 1
Affiliation  

This paper investigates undesirable limitations of widely used count data instrumental variable models. To overcome the limitations, I propose a partially identifying single-equation model that requires neither strong separability of unobserved heterogeneity nor a triangular system. Sharp bounds (identified sets) of structural features are characterised by conditional moment inequalities. Numerical examples show that the size of an identified set can be very small when the support of an outcome is rich or instruments are strong. An algorithm for estimation and inference is presented. I illustrate the usefulness of the proposed model in an empirical application to effects of supplemental insurance on healthcare utilisation.

中文翻译:

不可分计数数据工具变量模型中的部分识别

本文研究了广泛使用的计数数据工具变量模型的不良局限性。为了克服这些局限性,我提出了一种部分识别单方程模型,该模型既不需要强烈的不可观测异质性可分离性,也不需要三角系统。结构特征的尖锐边界(确定的集合)以条件矩不等式为特征。数值示例表明,当结果的支持丰富或工具强大时,已识别集合的大小可能很小。提出了一种估计和推理算法。我举例说明了所提出的模型在对补充保险对医疗保健利用的影响的经验应用中的有用性。
更新日期:2019-12-20
down
wechat
bug