Victor Chernozhukov

TL;DR: In this paper, the authors discuss conceptually high dimensional sparse econometric models as well as estimation of these models using L1-penalization and post-L1-Penalization methods.

TL;DR: In this paper, the authors show that these estimates can always be improved with no harm using rearrangement techniques, and that the rearranged lower and upper end-point functions are shorter in length in common norms than the original interval and cover the target function with probability greater or equal to the pre-specified level.

TL;DR: High-dimensional versions of standard estimation problems in statistics and econometrics, such as: estimation of conditional moment models with missing data, estimation of structural utilities in games of incomplete information and estimation of treatment effects in regression models with non-linear link functions are applied.

TL;DR: In this article, the authors look at the theory and empirics of extremal quantiles in economics, in particular value-at-risk, and discuss their applications in finance and risk management, efficiency analysis in industrial organization, and inference in structural auction models.

TL;DR: In this article, the authors propose an inference method for quantile and quantile effect (QE) functions for descriptive and causal analysis due to their natural and intuitive interpretation, and show that these functions are important tools for causal analysis.