CAREER: Identification as Optimization

使用数据来回答经济学中的问题，需要用更强的假设来维持假设，从而得出更有力的结论。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；目前还没有可用的方法。这项研究项目将开发新的方法，可以用来更好地控制假设的强度和正确结论之间的权衡。该研究项目的结果将为研究人员提供改善实证经济研究的工具，从而为政策制定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为以证据为基础的经济政策分析提供更好和更可靠的工具，从而促进国家经济繁荣。该研究项目还提供了教学进展，将经济学更好地整合到更广泛的STEM数据科学、计算机科学和应用数学教育中。该项目的结果还将确立美国在开发更好的经济分析工具方面的全球领先地位。识别是经验经济学和许多其他领域的一个关键概念。它表现出一种内在的权衡：更强的结论需要更强的假设，但更强的假设可能会导致错误的结论。实证研究人员在解决这一权衡问题上的偏好不同。拟议研究的目标是扩大可用方法的选择范围，使研究人员在探索假设-结论边界方面有更大的灵活性。所提出的研究将通过将辨识问题转化为优化问题来实现这一目标。这将抽象的识别问题转化为使受约束的函数最大化的具体数学运算。这使得应用数学和运筹学的计算和理论工具的应用成为可能。这项研究将把这种识别为优化的联系应用于三个不同的实证问题：(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)辅助变量和回归不连续设计中的外推。这一研究项目的结果将为实证研究提供更好的方法，从而更好地为经济政策制定提供投入。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.