no code implementations • 26 Jan 2024 • Denis Chetverikov, Magne Mogstad, Pawel Morgen, Joseph Romano, Azeem Shaikh, Daniel Wilhelm
Second, we review methods for estimation and inference in regressions involving ranks.
no code implementations • 16 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.
no code implementations • 24 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.
no code implementations • 18 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.
no code implementations • 26 Dec 2022 • Denis Chetverikov, Elena Manresa
In this paper, we develop spectral and post-spectral estimators for grouped panel data models.
no code implementations • 6 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.
no code implementations • 17 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
no code implementations • 30 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).
4 code implementations • 30 Jul 2016 • Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, James Robins
Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools.