变化-变化模型用于有顺序选择模型中的报告不足
Changes-in-Changes for Ordered Choice Models with Underreporting
AI总结：
本文提出一种差分-in-差分框架，用于处理存在报告不足的离散有序结果，通过非参数界分析和半参数模型估计报告不足下的处理效应。
摘要：
我们开发了一种差分-in-差分框架，用于离散有序结果受报告不足影响的情况。此类结果常见于自我报告调查中涉及社会不受欢迎或受污名化行为的数据，受访者可能隐瞒真实行为。对于一种已被证明可以等价于阈值跨越表示的离散变化-变化模型，我们推导了在结果存在报告不足时，事实和反事实结果分布以及相关分位数处理效应的非参数界。这些界在附加支持条件下被证明在所有结果水平上都是尖锐的，我们提出了合适的估计和bootstrap推断程序。在扩展部分，我们还考虑了一种半参数报告不足模型，允许点识别和估计分布处理效应。作为应用，我们研究了娱乐性大麻合法化对美国几个州八年级学生消费行为的影响。
英文摘要：
We develop a Difference-in-Differences framework for discrete, ordered outcomes subject to underreporting. Such outcomes commonly arise in self-reported surveys on socially undesirable or stigmatized behaviors, where respondents may conceal their true behavior. For a discrete Changes-in-Changes model that is shown to admit an equivalent threshold-crossing representation, we derive nonparametric bounds for the counterfactual and factual outcome distributions as well as for the associated quantile treatment effects when outcomes are underreported. These bounds are shown to be sharp uniformly across outcome levels under additional support conditions, and we propose suitable estimation and bootstrap inference procedures. In an extension, we also consider a semiparametric underreporting model that allows to point identify and estimate distributional treatment effects. As an application, we investigate the impact of recreational marijuana legalization on the consumption behavior of 8th-grade students in several U.S. states.
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。
