Robust Inference on Counterfactuals

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。之所以需要这种方法，是因为试验替代政策的成本太高或不可能。在为这种政策评估开发数据就绪模型时，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，政策预测可能对这些假设非常敏感。研究人员和政策制定者需要一些方法来帮助理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，劳动，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中开发的工具。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上为STEM教育做出贡献，包括运筹学和统计学。本研究的目标是为结构计量经济模型中的反事实问题开发稳健的、计算上易于处理的估计和推断方法。这些方法将是稳健的，因为当一些分布或函数形式的假设被放松时，它们将表征模型预测的反事实集，但模型的其他特征更强烈地扎根于经济理论。为了提供计算的易处理性，研究将建立在凸规划方法的分布鲁棒优化（DRO）在运筹学领域的最新进展。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模型的重要显着特征，包括内隐性，定点约束和未观察到的异质性。在本研究过程中开发的推理程序也将有助于广泛的文献部分确定的半参数计量经济模型。该方法将专门用于差异化产品需求的领先模型、单代理行为的主力动态模型以及多代理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，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.