Robust Inference on Counterfactuals

反事实的稳健推理

• 批准号: 1919034
• 负责人: Timothy Christensen
• 金额: $ 23.25万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1919034&HistoricalAwards=false
• 关键词: Robust; Inference; Counterfactuals

Large scale and detailed econometric models are used to evaluate the effects of economic and social policies before these policies are implemented. This approach is needed because experimenting with alternative policies is too costly or not possible. In developing data-ready models for such policy evaluation, model-builders often make several restrictive assumptions without the guidance of economic theory. However, policy predictions can be very sensitive to such assumptions. Researchers and policy makers need methods to help understand to what extent policy predictions are driven by economically grounded assumptions or on restrictive assumptions. The proposed research project will develop econometric tools that allow researchers to judge whether policy predictions are based on economically grounded theory or on restrictive assumptions. Researchers studying education, labor, public health, antitrust, trade, development, and environmental policies, among others, will use the tools developed in this project. This research project will therefore aid in formulating policies to improve social outcomes including welfare, the efficient provision of goods and services, and economic growth and competitiveness. The research will also contribute at a technical level to STEM education including operations research and statistics. The goal of the proposed research is to develop robust, computationally tractable estimation and inference methods for counterfactuals in structural econometric models. The methods will be robust in the sense that they will characterize the set of counterfactuals predicted by the model when some distributional or functional form assumptions are relaxed but other features of the model more strongly grounded in economic theory are maintained. To provide computational tractability, the research will build on recent advances in convex programming methods developed in the field of distributionally robust optimization (DRO) in operations research. The research will advance techniques and inference procedures developed in DRO to accommodate important distinguishing features of structural econometric models, including endogeneity, fixed-point constraints, and unobserved heterogeneity. The inference procedures developed during this research will also contribute broadly to the literature on partially identified semi-parametric econometric models. The methods will be specialized to leading models of demand for differentiated products, workhorse dynamic models of single-agent behavior, and static and dynamic models of strategic interactions between multiple agents.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.

在经济和社会政策实施之前，使用大规模和详细的计量经济模型来评估这些政策的效果。需要这种方法，因为尝试替代政策成本太高或者不可能。在开发用于此类政策评估的数据就绪模型时，模型构建者经常在没有经济理论指导的情况下做出一些限制性假设。然而，政策预测可能对此类假设非常敏感。研究人员和政策制定者需要方法来帮助了解政策预测在多大程度上是由经济基础假设或限制性假设驱动的。拟议的研究项目将开发计量经济学工具，使研究人员能够判断政策预测是基于经济基础理论还是基于限制性假设。研究教育、劳工、公共卫生、反垄断、贸易、发展和环境政策等的研究人员将使用该项目开发的工具。因此，该研究项目将有助于制定政策，以改善社会成果，包括福利、有效提供商品和服务以及经济增长和竞争力。该研究还将在技术层面为 STEM 教育做出贡献，包括运筹学和统计学。拟议研究的目标是为结构计量经济学模型中的反事实开发稳健的、计算上易于处理的估计和推理方法。这些方法将是稳健的，因为当放松一些分布或函数形式假设但保持模型的其他更牢固地基于经济理论的特征时，它们将表征模型预测的反事实集。为了提供计算的易处理性，该研究将建立在运筹学中分布鲁棒优化（DRO）领域开发的凸规划方法的最新进展的基础上。该研究将推进 DRO 中开发的技术和推理程序，以适应结构计量经济学模型的重要区别特征，包括内生性、定点约束和不可观察的异质性。本研究期间开发的推理程序也将对部分确定的半参数计量经济模型的文献做出广泛贡献。这些方法将专门用于差异化产品的需求领先模型、单主体行为的主力动态模型以及多个主体之间战略交互的静态和动态模型。该奖项反映了 NSF 的法定使命，并通过评估被认为值得支持利用基金会的智力优势和更广泛的影响审查标准。

项目成果

Counterfactual Sensitivity and Robustness

反事实敏感性和稳健性

• DOI: 10.3982/ecta17232
• 发表时间: 2023
• 期刊: Econometrica
• 影响因子: 6.1
• 作者: Christensen, Timothy;Connault, Benjamin
• 通讯作者: Connault, Benjamin

Adaptive Estimation and Uniform Confidence Bands for Nonparametric Structural Functions and Elasticities

非参数结构函数和弹性的自适应估计和均匀置信带

• 发表时间: 2024
• 期刊: The review of economic studies
• 作者: Chen, Xiaohong;Christensen, Timothy;Kankanala, Sid
• 通讯作者: Kankanala, Sid

Existence and uniqueness of recursive utilities without boundedness

无界递归效用的存在唯一性

• DOI: 10.1016/j.jet.2022.105413
• 发表时间: 2022
• 期刊: Journal of Economic Theory
• 影响因子: 1.6
• 作者: Christensen, Timothy M.