Collaborative Research: Robust Inference and Computational Methods for Optimal Values of Nonlinear Programs

协作研究：非线性程序最优值的鲁棒推理和计算方法

• 批准号: 1824375
• 负责人: Francesca Molinari
• 金额: $ 17.7万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1824375&HistoricalAwards=false
• 关键词: Collaborative; Research; Robust; Inference; Computational

Empirical research in the social sciences often entails estimating and drawing robust inference about optimal values of nonlinear programs. Examples include, but are not limited to, the analysis of causal effects of economic policies; features of the distribution of counterfactual outcomes (e.g. optimal reserve prices and optimal revenues) under weak assumptions; counterfactual vote shares and seats assignments; welfare effects of policy interventions; demand extrapolation and welfare analysis subject to rationality constraints; maximum and minimum responses to monetary policies. This research aims at establishing a general, formal framework and providing a methodology for estimation and robust inference on optimal values of nonlinear programs under weak restrictions on the underlying process that has generated the observable data. Recognizing that the computational feasibility of the method is crucial for its applicability and usefulness for empirical researchers and society more broadly, the investigators deliver algorithms for computation of the proposed estimators and robust confidence intervals. This research also delivers a collection of portable computer programs implementing the methodology that will be shared with the community openly and free of charges or restrictions.This research aims at developing robust inference procedures and computational methods for parameters in econometric models that are characterized as optimal values of nonlinear programs. Making inference on such functionals is nontrivial because subtle features of the underlying optimization problem may affect inference. For example, the optimal solution may not be unique, may be unique but only weakly identified, or may be characterized by intricate constraints. Due to these challenging features, existing methods often impose assumptions such as constraint qualifications on the underlying optimization problem. These are hard to verify in practice. This research aims at developing inference methods that place very little structure on the optimization problem. Further, the project aims at developing and investigating the convergence properties of a computational method that can be used to implement the procedure. Nonlinear programs often involve black-box functions that are computed by simulation or by solving a complex structural model. The algorithm developed in this project, which is based on the response surface method, mitigates the computational cost by constructing flexible approximations to such functions and adaptively drawing evaluation points to regions that are highly relevant for finding the optimal value.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.

社会科学中的实证研究通常需要对非线性规划的最优值进行估计和得出稳健的推论。例子包括但不限于对经济政策的因果效应的分析；在弱假设下反事实结果(例如最优储备价格和最优收入)的分布特征；反事实的投票权份额和席位分配；政策干预的福利效应；受理性约束的需求外推和福利分析；对货币政策的最大和最小反应。这项研究旨在建立一个通用的形式化框架，并提供一种方法，用于估计和稳健推断在产生可观测数据的潜在过程的弱约束下的非线性规划的最优值。认识到该方法的计算可行性对于其对经验研究人员和更广泛的社会的适用性和有用性至关重要，研究人员提出了计算所建议的估计器和稳健可信区间的算法。这项研究还提供了一系列可移植的计算机程序，实现了将与社区公开和免费共享的方法。本研究旨在开发稳健的推理程序和计算方法，用于计量经济模型中的参数，这些参数被表征为非线性规划的最优值。对这样的泛函进行推理是不容易的，因为基本优化问题的微妙特征可能会影响推理。例如，最优解可能不是唯一的，可能是唯一的但只是弱识别的，或者可能具有复杂的约束特征。由于这些具有挑战性的特点，现有的方法经常在底层优化问题上强加约束条件等假设。这些都很难在实践中得到验证。这项研究的目的是开发推理方法，使优化问题只有很少的结构。此外，该项目的目的是开发和研究可用于实施该程序的计算方法的收敛特性。非线性程序通常涉及通过模拟或通过求解复杂结构模型来计算的黑盒函数。在这个项目中开发的算法基于响应面方法，通过对这些函数构建灵活的近似并自适应地将评估点绘制到与找到最优值高度相关的区域来降低计算成本。该奖项反映了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。

项目成果

Local regression smoothers with set-valued outcome data

使用设定值结果数据进行局部回归平滑器

• DOI: 10.1016/j.ijar.2020.10.005
• 发表时间: 2021
• 期刊: International Journal of Approximate Reasoning
• 影响因子: 3.9
• 作者: Li, Qiyu;Molchanov, Ilya;Molinari, Francesca;Peng, Sida
• 通讯作者: Peng, Sida

Confidence Intervals for Projections of Partially Identified Parameters

• DOI: 10.3982/ecta14075
• 发表时间: 2019-07-01
• 期刊: ECONOMETRICA
• 影响因子: 6.1
• 作者: Kaido, Hiroaki;Molinari, Francesca;Stoye, Jorg
• 通讯作者: Stoye, Jorg