Victor Chernozhukov

TL;DR: This paper analyzed data from the 2016 American Community Survey using a high-dimensional wage regression and applying double lasso to quantify heterogeneity in the gender wage gap, finding that the gap varied substantially across women and was driven primarily by marital status, having children at home, race, occupation, industry, and educational attainment.

TL;DR: The central limit theorem and the multiplier bootstrap can be used for high dimensional estimation, multiple hypothesis testing, and adaptive specification testing and are of interest in numerous econometric and statistical applications.

TL;DR: In this article, Chernozhukov, Fernandez-Val, and Kowalski introduce a censored dependent variable, an endogenous independent variable, or both, which can be used in the context of economic forecasting.

TL;DR: Quantile and quantile effect functions are important tools for descriptive and causal analysis due to their natural and intuitive interpretation.

TL;DR: This work establishes uniform estimation rates and sparsity guarantees of the square-root estimator in a random design under approximate sparsity conditions that might be of independent interest for related problems in high-dimensions.