Econometric Methods for Models with Covariate Adaptive Randomization and Partial Identification
具有协变量自适应随机化和部分识别的模型的计量经济学方法
基本信息
- 批准号:1729280
- 负责人:
- 金额:$ 16.38万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2017
- 资助国家:美国
- 起止时间:2017-08-15 至 2020-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This research develops new econometric methods to address two types of problems recently faced by applied researchers in several areas of economics and other social sciences. First, development economists often employ randomized control experiments using covariate adaptive randomization to "balance" the impact of the underlying observed covariates. Standard inference methods are typically used in this setting, but they can produce invalid results. In light of this, the first part of the research develops novel inference methods that are both valid and easy to implement. Second, partially identified models have been widely used in labor economics and industrial organization to incorporate missing data or multiplicity of equilibria, but the literature has not adequately addressed how to test the validity of a subset of the moment conditions. The second part of the research thus develops a new method to address this problem. In particular, the hypothesis test considered in this project can be also used to evaluate whether a certain instrumental variable is valid or not in a model with moment equalities and inequalities.This research develops new theories and methods for analyzing two econometric models. The first two projects study inference on the average treatment effect in randomized control experiments that use covariate adaptive randomization. The first project considers experiments in which there are multiple treatments, and the assignment is not necessarily evenly distributed among the control and the treatment groups. The project proposes regression-based inference methods that are shown to be valid, to have excellent power properties, and to be easy to implement. The second project considers experiments with assignment occurring at a group or cluster level, e.g., classroom, village, etc. The statistical dependence among individuals within each group requires developing new methodologies. Finally, the third project considers an inference problem in a partially identified model defined by moment equalities and inequalities. In this context, the goal is to test the validity of a subset of the moment conditions, while maintaining the validity of the remaining ones. This project develops a new bootstrap-based method that is shown to be valid and has good power properties.
本研究开发了新的计量经济学方法,以解决经济学和其他社会科学几个领域的应用研究人员最近面临的两类问题。首先,发展经济学家经常采用随机对照实验,使用协变量自适应随机化来“平衡”潜在的观察协变量的影响。标准推理方法通常用于此设置,但它们可能会产生无效结果。有鉴于此,研究的第一部分开发了新颖的推理方法,既有效又易于实现。其次,部分识别模型已被广泛用于劳动经济学和产业组织纳入缺失数据或多重均衡,但文献没有充分解决如何测试的时刻条件的子集的有效性。因此,研究的第二部分开发了一种新的方法来解决这个问题。特别地,本课题所考虑的假设检验也可以用来评价具有矩等式和矩不等式的模型中某个工具变量是否有效,本研究为分析两个计量经济模型开拓了新的理论和方法。前两个项目研究使用协变量自适应随机化的随机对照实验中平均治疗效果的推断。第一个项目考虑的实验中,有多个治疗,分配不一定是均匀分布在控制组和治疗组。该项目提出了基于回归的推理方法,这些方法被证明是有效的,具有出色的功率特性,并且易于实现。第二个项目考虑在组或集群级别进行分配的实验,例如,每个群体内的个人之间的统计依赖性需要开发新的方法。最后,第三个项目考虑了一个由矩等式和不等式定义的部分识别模型中的推理问题。在这种情况下,目标是测试矩条件的子集的有效性,同时保持其余条件的有效性。本计画发展一种新的基于自举的方法,其结果显示是有效的,且具有良好的功率特性。
项目成果
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Inference under covariate‐adaptive randomization with multiple treatments
多种治疗的协变量自适应随机化下的推断
- DOI:10.3982/qe1150
- 发表时间:2019
- 期刊:
- 影响因子:1.8
- 作者:Bugni, Federico A.;Canay, Ivan A.;Shaikh, Azeem M.
- 通讯作者:Shaikh, Azeem M.
Inference in dynamic discrete choice problems under local misspecification
局部错误指定下动态离散选择问题的推理
- DOI:10.3982/qe917
- 发表时间:2019
- 期刊:
- 影响因子:1.8
- 作者:Bugni, Federico A.;Ura, Takuya
- 通讯作者:Ura, Takuya
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Federico Bugni其他文献
A Binary IV Model for Persuasion: Profiling Persuasion Types among Compliers *
说服的二元 IV 模型:分析遵守者的说服类型*
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Arthur Zeyang;Scott Gehlbach;Robert Gulotty;Molly Offer;Alexander Torgovitsky;Eric Auerbach;Stéphane Bonhomme;Federico Bugni;Joshua Byun;Ma;Gustavo Diaz;Wayne Yuan Gao;Justin Grimmer;Peter Hull;K. Imai;Sung Jae Jun;Nadav Kunievsky;Xinran Li;Jonathan Mummolo;Maggie Penn;Kirill Ponomarev;James Robinson;Jonathan Roth;F. Ruggieri;Azeem M. Shaikh;Tymon Słoczyński;Joshua Ka;Chun Shea;Liyang Sun;Max Tabord;Christopher Walters;Linbo Wang;Yiqing Xu;Teppei Yamamoto - 通讯作者:
Teppei Yamamoto
Federico Bugni的其他文献
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{{ truncateString('Federico Bugni', 18)}}的其他基金
Collaborative Research: Extending the Scope of Inference in Partially Identified Models
协作研究:扩展部分识别模型的推理范围
- 批准号:
1123771 - 财政年份:2011
- 资助金额:
$ 16.38万 - 项目类别:
Standard Grant
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