CAREER: Identification as Optimization

职业：识别作为优化

• 批准号: 1846832
• 负责人: Alexander Torgovitsky
• 金额: $ 44.9万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1846832&HistoricalAwards=false
• 关键词: CAREER; Identification; Optimization

Using data to answer questions in economics requires maintaining assumptions with stronger assumptions leading to stronger conclusions. However, stronger assumptions are also more likely to lead to wrong conclusions. Researchers therefore need methods that bridge these tensions; methods not currently available. This research project will develop new methods that can be used to provide better control on the tradeoff between the strength of assumptions and correct conclusions. The results of the research project will provide researchers with tools to improve empirical economic research and therefore provide policy makers with more accurate policy inputs. The research project therefore advances national economic prosperity by providing better and more reliable tools for evidence-based economic policy analysis in many areas such as antitrust enforcement. The research project also provides pedagogical advances that better integrate economics into a broader STEM education on data science, computer science, and applied mathematics. The results of this project will also establish the U.S. as the global leader in developing better tools for economic analyses.Identification is a critical concept in empirical economics and many other fields. It exhibits an inherent tradeoff: Stronger conclusions require stronger assumptions, but stronger assumptions may lead to wrong conclusions. Empirical researchers differ in their preferences for resolving this tradeoff. The goal of the proposed research is to widen the choice of available methodology in a way that gives researchers more flexibility in exploring the assumptions-conclusions frontier. The proposed research will achieve this goal by casting the identification problem as an optimization problem. This transforms the abstract question of identification into the concrete mathematical operation of maximizing a function subject to constraints. This enables the application of computational and theoretical tools from applied mathematics and operations research. The proposed research will apply this identification-as-optimizations link to three different empirical problems: (1) nonparametric discrete choice modeling; (2) sensitivity analysis in dynamic programming models; and (3) extrapolation in instrumental variable and regression discontinuity designs. The results of this research project will provide enhanced method for empirical research, hence better inputs into economic policy making.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

使用数据来回答经济学中的问题需要保持假设，更强的假设会导致更强的结论。 然而，更强的假设也更有可能导致错误的结论。 因此，研究人员需要弥合这些紧张关系的方法;目前还没有方法。 该研究项目将开发新的方法，可用于更好地控制假设强度和正确结论之间的权衡。 研究项目的结果将为研究人员提供改进实证经济研究的工具，从而为决策者提供更准确的政策投入。 因此，该研究项目通过在反垄断执法等许多领域提供更好和更可靠的循证经济政策分析工具，促进国家经济繁荣。 该研究项目还提供了教学进步，更好地将经济学融入更广泛的数据科学，计算机科学和应用数学的STEM教育中。 该项目的成果还将使美国成为开发更好的经济分析工具的全球领导者。识别是实证经济学和许多其他领域的关键概念。 它表现出一种内在的权衡：更强的结论需要更强的假设，但更强的假设可能导致错误的结论。 实证研究人员在解决这一权衡问题时的偏好各不相同。 拟议的研究的目标是扩大可用的方法的选择，使研究人员在探索结论的前沿更灵活的方式。 建议的研究将实现这一目标，铸造的识别问题作为一个优化问题。 这将识别的抽象问题转化为具体的数学运算，即最大化受约束的函数。 这使得应用数学和运筹学的计算和理论工具的应用。 拟议的研究将应用这种识别优化链接到三个不同的经验问题：（1）非参数离散选择建模;（2）动态规划模型中的灵敏度分析;（3）工具变量和回归不连续性设计中的外推。 该研究项目的结果将为实证研究提供更好的方法，从而为经济政策制定提供更好的投入。该奖项反映了NSF的法定使命，并且通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Policy evaluation with multiple instrumental variables

多工具变量的政策评估

• DOI: 10.1016/j.jeconom.2024.105718
• 发表时间: 2024
• 期刊: Journal of Econometrics
• 影响因子: 6.3
• 作者: Mogstad, Magne;Torgovitsky, Alexander;Walters, Christopher R.
• 通讯作者: Walters, Christopher R.

Nonparametric Estimates of Demand in the California Health Insurance Exchange

加州健康保险交易所需求的非参数估计

• DOI: 10.3982/ecta17215
• 发表时间: 2023
• 期刊: Econometrica
• 影响因子: 6.1
• 作者: Tebaldi, Pietro;Torgovitsky, Alexander;Yang, Hanbin
• 通讯作者: Yang, Hanbin

ivmte: An R Package for Extrapolating Instrumental Variable Estimates Away From Compliers

ivmte：一个 R 包，用于从编译器中推断工具变量估计值

• 发表时间: 2023
• 期刊: Observational studies
• 作者: Shea, Joshua;Torgovitsky, Alexander
• 通讯作者: Torgovitsky, Alexander

Inference for Large‐Scale Linear Systems With Known Coefficients

具有已知系数的大规模线性系统的推理

• DOI: 10.3982/ecta18979
• 发表时间: 2023
• 期刊: Econometrica
• 影响因子: 6.1
• 作者: Fang, Zheng;Santos, Andres;Shaikh, Azeem M.;Torgovitsky, Alexander
• 通讯作者: Torgovitsky, Alexander

ivcrc: An instrumental-variables estimator for the correlated random-coefficients model

ivcrc：相关随机系数模型的工具变量估计器

• DOI: 10.1177/1536867x221124449
• 发表时间: 2022
• 期刊: The Stata Journal: Promoting communications on statistics and Stata
• 作者: Benson, David;Masten, Matthew A.;Torgovitsky, Alexander
• 通讯作者: Torgovitsky, Alexander