Victor Chernozhukov

This paper provides an overview of how innovations in "data mining" can be adapted and modified to provide high-quality inference about model parameters.

We consider estimation of policy relevant treatment effects in a data-rich environment where there may be many more control variables available than there are observations.

In this article, we review quantile models with endogeneity.

We present the clrbound, clr2bound, clr3bound and clrtest commands for estimation and inference developed by Chernozhukov et al. (2013).

This package includes various commands. clr2bound compute two-sided bound estimates using Bonferroni's inequality. clrbound compute a one-sided bound estimate. clrtest tests the hypothesis that the maximum of lower intersection bounds is nonpositive. clr3bound compute two-sided bound estimates by inverting clrtest.

We propose robust methods for inference on the effect of a treatment variable on a scalar outcome in the presence of very many controls.

We develop uniformly valid confidence regions for a regression coefficient in a high-dimensional sparse LAD (least absolute deviation or median) regression model.

In this article, we review quantile models with endogeneity.

In this article the authors develop modelling and inference tools for counterfactual distributions based on regression methods.

This paper gives identification and estimation results for marginal effects in nonlinear panel models.

We develop a practical and novel method for inference on intersection bounds, namely bounds defined by either the infimum or supremum of a parametric or nonparametric function, or equivalently, the value of a linear programming problem with a potentially infinite constraint set.

This paper considers the problem of inference on a class of sets describing a collection of admissible models as solutions to a single smooth inequality.

We develop results for the use of Lasso and post-Lasso methods to form first-stage predictions and estimate optimal instruments in linear instrumental variables (IV) models with many instruments, p.

This paper derives a central limit theorem for the maximum of a sum of high dimensional random vectors.

This paper provides inference methods for best linear approximations to functions which are known to lie within a band.

This paper develops a new direct approach to approximating suprema of general empirical processes by a sequence of suprema of Gaussian processes, without taking the route of approximating empirical processes themselves in the sup-norm.

In parametric models a sufficient condition for local idenfication is that the vector of moment is differentiable at the true parameter with full rank derivative matrix. This paper shows that additional conditions are often needed in nonlinear, nonparametric models to avoid nonlinearities overwhelming linear effects.

This paper provides a practical and novel method for inference on intersection bounds, namely bounds defined by either the infimum or supremum of a parametric or nonparametric function, or equivalently, the value of a linear programming problem with a potentially infinite constraint set.

The authors develop inference procedures for policy analysis based on regression methods.