Victor Chernozhukov

TL;DR: The authors used Lasso to mitigate the curse of dimensionality in estimating the aver-age expenditure share from cross-section data and estimate bounds on consumer surplus (BCS) using a novel double/debiased Lasso method.

TL;DR: This paper considers inference for a function of a parameter vector in a partially identified model with many moment inequalities, and shows that this method controls asymptotic size uniformly over a large class of data generating processes despite the partially identified many moment inequality setting.

TL;DR: The authors consider the problem of testing many moment inequalities where the number of moment inequalities, denoted by $p$, is possibly much larger than the sample size $n, and show that all of these tests are (minimax) optimal in the sense that they are uniformly consistent against alternatives whose "distance" from the null is larger than a threshold.

TL;DR: This paper derives Gaussian and bootstrap approximations for the probabilities that a root-n rescaled sample average of Xi is in A, and shows that the approximation error converges to zero even if p=pn-> infinity and p>>n; in particular, p can be as large as O(e^(Cn^c) for some constants c,C>0.

TL;DR: In this article, the authors consider the problem of inference on a class of sets describing a collection of admissible models as solutions to a single smooth inequality, and they construct convenient and powerful confidence regions based on the weighted likelihood ratio and weighted Wald statistics.