Computing moment inequality models using constrained optimization
针对矩不等式模型计算量大的问题，利用无约束与约束优化的等价形式，提出新方法计算识别集和置信集，相比传统网格搜索显著提升解的质量并节省计算资源。
Summary Inference for moment inequality models is computationally demanding and often involves time-consuming grid search. By exploiting the equivalent formulations between unconstrained and constrained optimization, we establish new ways to compute the identified set and its confidence set in moment inequality models that overcome some of these computational hurdles. In simulations, using both linear and nonlinear moment inequality models, we show that our method significantly improves the solution quality and save considerable computing resources relative to conventional grid search. Our methods are user-friendly and can be implemented using a variety of canned software packages.