Search Results for author: Denis Chetverikov

Found 9 papers, 1 papers with code

Logit-based alternatives to two-stage least squares

no code implementations16 Dec 2023 Denis Chetverikov, Jinyong Hahn, Zhipeng Liao, Shuyang Sheng

We propose logit-based IV and augmented logit-based IV estimators that serve as alternatives to the traditionally used 2SLS estimator in the model where both the endogenous treatment variable and the corresponding instrument are binary.

Inference for Rank-Rank Regressions

no code implementations24 Oct 2023 Denis Chetverikov, Daniel Wilhelm

Second, we derive a general asymptotic theory for rank-rank regressions and provide a consistent estimator of the OLS estimator's asymptotic variance.

Standard errors when a regressor is randomly assigned

no code implementations18 Mar 2023 Denis Chetverikov, Jinyong Hahn, Zhipeng Liao, Andres Santos

In particular, when the regressor of interest is independent not only of other regressors but also of the error term, the textbook homoskedastic variance formula is valid even if the error term and auxiliary regressors exhibit a general dependence structure.

valid

Spectral and post-spectral estimators for grouped panel data models

no code implementations26 Dec 2022 Denis Chetverikov, Elena Manresa

In this paper, we develop spectral and post-spectral estimators for grouped panel data models.

Weighted-average quantile regression

no code implementations6 Mar 2022 Denis Chetverikov, Yukun Liu, Aleh Tsyvinski

In this paper, we introduce the weighted-average quantile regression framework, $\int_0^1 q_{Y|X}(u)\psi(u)du = X'\beta$, where $Y$ is a dependent variable, $X$ is a vector of covariates, $q_{Y|X}$ is the quantile function of the conditional distribution of $Y$ given $X$, $\psi$ is a weighting function, and $\beta$ is a vector of parameters.

regression

Nearly optimal central limit theorem and bootstrap approximations in high dimensions

no code implementations17 Dec 2020 Victor Chernozhukov, Denis Chetverikov, Yuta Koike

In this paper, we derive new, nearly optimal bounds for the Gaussian approximation to scaled averages of $n$ independent high-dimensional centered random vectors $X_1,\dots, X_n$ over the class of rectangles in the case when the covariance matrix of the scaled average is non-degenerate.

Probability Statistics Theory Statistics Theory 60F05, 62E17

Double/Debiased/Neyman Machine Learning of Treatment Effects

no code implementations30 Jan 2017 Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey

A more general discussion and references to the existing literature are available in Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016).

BIG-bench Machine Learning valid

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