“更好的聚束,更好的缺口”补充附录

Marinho Bertanha, A. McCallum, N. Seegert
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引用次数: 28

摘要

我们研究了弹性参数的聚类识别策略,该参数总结了智能体对一系列激励的斜率(扭结)或截距(缺口)变化的响应。当介质的分布完全灵活时,缺口识别弹性,而扭结不能识别弹性。我们对代理的分布提出了新的非参数和半参数识别假设,这些假设比目前文献中所做的假设弱。我们重新审视了聚束估计器的原始经验应用,发现我们较弱的识别假设导致了有意义的不同估计。我们提供Stata包束来实现我们的过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Supplemental Appendix to 'Better Bunching, Nicer Notching'
We study the bunching identification strategy for an elasticity parameter that summarizes agents' response to changes in slope (kink) or intercept (notch) of a schedule of incentives. A notch identifies the elasticity but a kink does not, when the distribution of agents is fully flexible. We propose new non-parametric and semi-parametric identification assumptions on the distribution of agents that are weaker than assumptions currently made in the literature. We revisit the original empirical application of the bunching estimator and find that our weaker identification assumptions result in meaningfully different estimates. We provide the Stata package bunching to implement our procedures.
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