Abstract

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 which 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.

中文翻译：

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使用约束优化计算矩不等式模型

摘要

矩不等式模型的推论在计算上要求很高，并且通常涉及耗时的网格搜索。通过利用无约束和有约束的优化之间的等效公式，我们建立了新的方法来计算矩不等式模型中的识别集及其置信度集，从而克服了一些计算障碍。在仿真中，同时使用线性和非线性矩不等式模型，我们表明，相对于传统的网格搜索，我们的方法显着提高了求解质量，并节省了可观的计算资源。我们的方法用户友好，可以使用多种罐头软件包来实现。