International Fellows

Victor Chernozhukov

MIT

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Selected Publications

Working Paper
LASSO-driven inference in time and space

We consider the estimation and inference in a system of high-dimensional regression equations allowing for temporal […]

Victor Chernozhukov, Wolfgang Härdle, Chen Huang, Weining Wang
20 June 2018 | CWP36/18

Latest version

LASSO-Driven Inference in Time and Space
Victor Chernozhukov, Wolfgang Härdle, Chen Huang, Weining Wang
29 April 2019 | CWP20/19
High-dimensional econometrics and regularized GMM

This chapter presents key concepts and theoretical results for analyzing estimation and inference in high-dimensional models. […]

Alexandre Belloni, Victor Chernozhukov, Denis Chetverikov, Christian Hansen, Kengo Kato
12 June 2018 | CWP35/18
Locally robust semiparametric estimation

We give a general construction of debiased/locally robust/orthogonal (LR) moment functions for GMM, where the derivative […]

Victor Chernozhukov, Juan Carlos Escanciano, Hidehiko Ichimura, Whitney K. Newey, James M. Robins
26 April 2018 | CWP30/18

Previous version

Locally robust semiparametric estimation
Victor Chernozhukov, Juan Carlos Escanciano, Hidehiko Ichimura, Whitney K. Newey
2 August 2016 | CWP31/16
Network and panel quantile effects via distribution regression

This paper provides a method to construct simultaneous confidence bands for quantile functions and quantile effects […]

Victor Chernozhukov, Ivan Fernandez-Val, Martin Weidner
22 March 2018 | CWP21/18

Latest version

Network and panel quantile effects via distribution regression
Victor Chernozhukov, Ivan Fernandez-Val, Martin Weidner
12 December 2018 | CWP70/18
Double/de-biased machine learning using regularized Riesz representers

We provide adaptive inference methods for linear functionals of L1-regularized linear approximations to the conditional expectation […]

Victor Chernozhukov, Whitney K. Newey, James Robins
2 March 2018 | CWP15/18
Exact and robust conformal inference methods for predictive machine learning with dependent data

We extend conformal inference to general settings that allow for time series data. Our proposal is […]

Victor Chernozhukov, Kaspar Wüthrich, Yinchu Zhu
2 March 2018 | CWP16/18
Generic machine learning inference on heterogenous treatment effects in randomized experiments

We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized […]

Victor Chernozhukov, Mert Demirer, Esther Duflo, Ivan Fernandez-Val
30 December 2017 | CWP61/17
Counterfactual analysis in R: a vignette

The R package Counterfactual implements the estimation and inference methods of Chernozhukov et al. (2013) for […]

Mingli Chen, Victor Chernozhukov, Ivan Fernandez-Val, Blaise Melly
30 December 2017 | CWP64/17
Extremal quantile regression: an overview

Extremal quantile regression, i.e. quantile regression applied to the tails of the conditional distribution, counts with […]

Victor Chernozhukov, Ivan Fernandez-Val, Tetsuya Kaji
30 December 2017 | CWP65/17
An exact and robust conformal inference method for counterfactual and synthetic controls

This paper introduces new inference methods for counterfactual and synthetic control methods for evaluating policy effects. […]

Victor Chernozhukov, Kaspar Wüthrich, Yu Zhu
26 December 2017 | CWP62/17
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