322 citations · 816 across the 25 of their papers we have counts for
26 papers
csranks: An R Package for Estimation and Inference Involving Ranks
Denis Chetverikov, Magne Mogstad, Pawel Morgen +3
This article introduces the R package csranks for estimation and inference involving ranks. First, we review methods for the construction of confidence sets for ranks, namely margi…
Inference for Rank-Rank Regressions
Denis Chetverikov, Daniel Wilhelm
The slope coefficient in a rank-rank regression is a popular measure of intergenerational mobility. In this article, we first show that commonly used inference methods for this slo…
Standard errors when a regressor is randomly assigned
Denis Chetverikov, Jinyong Hahn, Zhipeng Liao +1
We examine asymptotic properties of the OLS estimator when the values of the regressor of interest are assigned randomly and independently of other regressors. We find that the OLS…
Spectral and post-spectral estimators for grouped panel data models
Denis Chetverikov, Elena Manresa
In this paper, we develop spectral and post-spectral estimators for grouped panel data models. Both estimators are consistent in the asymptotics where the number of observations $N…
High-dimensional Data Bootstrap
Victor Chernozhukov, Denis Chetverikov, Kengo Kato +1
This article reviews recent progress in high-dimensional bootstrap. We first review high-dimensional central limit theorems for distributions of sample mean vectors over the rectan…
Weighted-average quantile regression
Denis Chetverikov, Yukun Liu, Aleh Tsyvinski
In this paper, we introduce the weighted-average quantile regression framework, , where is a dependent variable, is a vector of covariates,…
Selecting Penalty Parameters of High-Dimensional M-Estimators using Bootstrapping after Cross-Validation
Denis Chetverikov, Jesper Riis-Vestergaard Sørensen
We develop a new method for selecting the penalty parameter for -penalized M-estimators in high dimensions, which we refer to as bootstrapping after cross-validation. We…
Nearly optimal central limit theorem and bootstrap approximations in high dimensions
Victor Chernozhukov, Denis Chetverikov, Yuta Koike
In this paper, we derive new, nearly optimal bounds for the Gaussian approximation to scaled averages of independent high-dimensional centered random vectors ov…
Improved Central Limit Theorem and bootstrap approximations in high dimensions
Victor Chernozhukov, Denis Chetverikov, Kengo Kato +1
This paper deals with the Gaussian and bootstrap approximations to the distribution of the max statistic in high dimensions. This statistic takes the form of the maximum over compo…
High-Dimensional Econometrics and Regularized GMM
Alexandre Belloni, Victor Chernozhukov, Denis Chetverikov +2
This chapter presents key concepts and theoretical results for analyzing estimation and inference in high-dimensional models. High-dimensional models are characterized by having a…
Detailed proof of Nazarov's inequality
Victor Chernozhukov, Denis Chetverikov, Kengo Kato
The purpose of this note is to provide a detailed proof of Nazarov's inequality stated in Lemma A.1 in Chernozhukov, Chetverikov, and Kato (2017, Annals of Probability).
Double/Debiased/Neyman Machine Learning of Treatment Effects
Victor Chernozhukov, Denis Chetverikov, Mert Demirer +3
Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016) provide a generic double/de-biased machine learning (DML) approach for obtaining valid inferential statements ab…
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…
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…
Uniformly Valid Post-Regularization Confidence Regions for Many Functional Parameters in Z-Estimation Framework
Alexandre Belloni, Victor Chernozhukov, Denis Chetverikov +1
In this paper we develop procedures to construct simultaneous confidence bands for potentially infinite-dimensional parameters after model selection for general moment c…
Nonparametric instrumental variable estimation under monotonicity
Denis Chetverikov, Daniel Wilhelm
The ill-posedness of the inverse problem of recovering a regression function in a nonparametric instrumental variable model leads to estimators that may suffer from a very slow, lo…
Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings
Victor Chernozhukov, Denis Chetverikov, Kengo Kato
We derive strong approximations to the supremum of the non-centered empirical process indexed by a possibly unbounded VC-type class of functions by the suprema of the Gaussian and…
Central Limit Theorems and Bootstrap in High Dimensions
Victor Chernozhukov, Denis Chetverikov, Kengo Kato
This paper derives central limit and bootstrap theorems for probabilities that sums of centered high-dimensional random vectors hit hyperrectangles and sparsely convex sets. Specif…
Inference on causal and structural parameters using many moment inequalities
Victor Chernozhukov, Denis Chetverikov, Kengo Kato
This paper considers the problem of testing many moment inequalities where the number of moment inequalities, denoted by , is possibly much larger than the sample size . Ther…
Anti-concentration and honest, adaptive confidence bands
Victor Chernozhukov, Denis Chetverikov, Kengo Kato
Modern construction of uniform confidence bands for nonparametric densities (and other functions) often relies on the classical Smirnov-Bickel-Rosenblatt (SBR) condition; see, for…
Comparison and anti-concentration bounds for maxima of Gaussian random vectors
Victor Chernozhukov, Denis Chetverikov, Kengo Kato
Slepian and Sudakov-Fernique type inequalities, which compare expectations of maxima of Gaussian random vectors under certain restrictions on the covariance matrices, play an impor…
Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors
Victor Chernozhukov, Denis Chetverikov, Kengo Kato
We derive a Gaussian approximation result for the maximum of a sum of high-dimensional random vectors. Specifically, we establish conditions under which the distribution of the max…
Gaussian approximation of suprema of empirical processes
Victor Chernozhukov, Denis Chetverikov, Kengo Kato
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 approx…
Testing Regression Monotonicity in Econometric Models
Denis Chetverikov
Monotonicity is a key qualitative prediction of a wide array of economic models derived via robust comparative statics. It is therefore important to design effective and practical…