英文文献：Inference in partially identified models with many moment inequalities using Lasso-在部分识别模型与许多时刻不平等的推理使用拉索
英文文献作者：Federico A. Bugni,Mehmet Caner,Anders Bredahl Kock,Soumendra Lahiri
英文文献摘要：
This paper considers the problem of inference in a partially identified moment (in)equality model with possibly many moment inequalities. Our contribution is to propose a novel two-step new inference method based on the combination of two ideas. On the one hand, our test statistic and critical values are based on those proposed by Chernozhukov et al. (2014c) (CCK14, hereafter). On the other hand, we propose a new first step selection procedure based on the Lasso. Some of the advantages of our two-step inference method are that (i) it can be used to conduct hypothesis tests and to construct confidence sets for the true parameter value that is uniformly valid, both in underlying parameter _ and distribution of the data; (ii) our test is asymptotically optimal in a minimax sense and (iii) our method has better power than CCK14 in large parts of the parameter space, both in theory and in simulations. Finally, we show that the Lasso-based first step can be implemented with a thresholding least squares procedure that makes it extremely simple to compute.

研究了可能存在多个矩不等式的部分识别矩等式模型的推理问题。我们的贡献是提出了一种新的基于两步推理的新方法。一方面，我们的测试统计量和临界值是基于Chernozhukov等人(2014c) (CCK14，以下)提出的统计量和临界值。另一方面，我们提出了一个新的基于套索的第一步选择程序。我们的两步推理方法的一些优点是:(i)它可以用来进行假设检验，并构建真实参数值的置信集，该真参数值在基本参数_和数据分布上都是一致有效的;(ii)我们的测试在极大极小的意义上是渐近最优的，(iii)我们的方法在参数空间的大部分，在理论和仿真中都比CCK14有更好的能力。最后，我们证明了基于lasso的第一步可以通过阈值最小二乘程序来实现，这使得计算非常简单。