723 citations · 5.4k across the 110 of their papers we have counts for
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Extremal Quantile Regression: An Overview
Victor Chernozhukov, Iván Fernández-Val, Tetsuya Kaji
Extremal quantile regression, i.e. quantile regression applied to the tails of the conditional distribution, counts with an increasing number of economic and financial applications…
quantreg.nonpar: An R Package for Performing Nonparametric Series Quantile Regression
Michael Lipsitz, Alexandre Belloni, Victor Chernozhukov +1
The R package quantreg.nonpar implements nonparametric quantile regression methods to estimate and make inference on partially linear quantile models. quantreg.nonpar obtains point…
Counterfactual: An R Package for Counterfactual Analysis
Mingli Chen, Victor Chernozhukov, Iván Fernández-Val +1
The Counterfactual package implements the estimation and inference methods of Chernozhukov, Fernández-Val and Melly (2013) for counterfactual analysis. The counterfactual distribut…
Vector quantile regression beyond correct specification
Guillaume Carlier, Victor Chernozhukov, Alfred Galichon
This paper studies vector quantile regression (VQR), which is a way to model the dependence of a random vector of interest with respect to a vector of explanatory variables so to c…
Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes
Victor Chernozhukov, Iván Fernández-Val, Blaise Melly +1
Quantile and quantile effect functions are important tools for descriptive and causal analyses due to their natural and intuitive interpretation. Existing inference methods for the…
hdm: High-Dimensional Metrics
Victor Chernozhukov, Chris Hansen, Martin Spindler
In this article the package High-dimensional Metrics (\texttt{hdm}) is introduced. It is a collection of statistical methods for estimation and quantification of uncertainty in hig…
Double/Debiased Machine Learning for Treatment and Causal Parameters
Victor Chernozhukov, Denis Chetverikov, Mert Demirer +4
Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these metho…
Locally Robust Semiparametric Estimation
Victor Chernozhukov, Juan Carlos Escanciano, Hidehiko Ichimura +2
Many economic and causal parameters depend on nonparametric or high dimensional first steps. We give a general construction of locally robust/orthogonal moment functions for GMM, w…
Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk
Alexandre Belloni, Mingli Chen, Victor Chernozhukov
We propose two types of Quantile Graphical Models (QGMs) --- Conditional Independence Quantile Graphical Models (CIQGMs) and Prediction Quantile Graphical Models (PQGMs). CIQGMs ch…
On cross-validated Lasso in high dimensions
Denis Chetverikov, Zhipeng Liao, Victor Chernozhukov
In this paper, we derive non-asymptotic error bounds for the Lasso estimator when the penalty parameter for the estimator is chosen using -fold cross-validation. Our bounds impl…
High-Dimensional Metrics in R
Victor Chernozhukov, Chris Hansen, Martin Spindler
The package High-dimensional Metrics (\Rpackage{hdm}) is an evolving collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approxim…