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Arellano-Bond lasso estimator for dynamic linear panel models

Chernozhukov, Victor; Fernández-Val, Iván; Huang, Chen; … - 2024

most informative moment conditions, motivating the following approach to remove the bias: First, apply LASSO to the cross …

Persistent link: https://www.econbiz.de/10014581834

Program evaluation with high-dimensional data

Belloni, Alexandre; Chernozhukov, Victor; … - 2013

We consider estimation of policy relevant treatment effects in a data-rich environ ment where there may be many more control variables available than there are observations. In addition to allowing many control variables, the setting we consider allows heterogeneous treatment effects, endogenous...

Persistent link: https://www.econbiz.de/10010368188

Program evaluation with high-dimensional data

Belloni, Alexandre; Chernozhukov, Victor; Fernández … - 2013

continuum of target parameters is of interest and the Lasso-type or post-Lasso type methods are used to estimate a continuum of … establish rate and consistency results for continua of Lasso or post-Lasso type methods for estimating continua of the (nuisance …

Persistent link: https://www.econbiz.de/10010368202

Program evaluation with high-dimensional data

Belloni, Alexandre; Chernozhukov, Victor; … - 2014

for a continuum of target parameters and for Lasso-type or Post-Lasso type methods to be used as estimators of a continuum … continua of Lasso or Post-Lasso type estimators for continua of (nuisance) regression functions and provide practical …

Persistent link: https://www.econbiz.de/10011282653

The lasso for high-dimensional regression with a possible change-point

Lee, Sokbae; Seo, Myung Hwan; Shin, Youngki - 2014

the Lasso estimator of regression coefficients as well as the threshold parameter. Our Lasso estimator not only selects …-asymptotic oracle inequalities for both the prediction risk and the l1 estimation loss for regression coefficients. Since the Lasso …

Persistent link: https://www.econbiz.de/10011282656

Program evaluation with high-dimensional data

Belloni, Alexandre; Chernozhukov, Victor; … - 2015

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data-rich environments. We can handle very many control variables, endogenous receipt of treatment,...

Persistent link: https://www.econbiz.de/10011445754

LASSO-driven inference in time and space

Chernozhukov, Victor; Härdle, Wolfgang; Huang, Chen; … - 2018

of large-scale regressions with LASSO is applied to reduce the dimensionality, and an overall penalty level is carefully …

Persistent link: https://www.econbiz.de/10011941488

LASSO-driven inference in time and space

Chernozhukov, Victor; Härdle, Wolfgang; Huang, Chen; … - 2019

of regressions with many regressors using LASSO (Least Absolute Shrinkage and Selection Operator) is applied for variable …

Persistent link: https://www.econbiz.de/10012146373

Minimax semiparametric learning with approximate sparsity

Bradic, Jelena; Chernozhukov, Victor; Newey, Whitney K.; … - 2021

This paper is about the ability and means to root-n consistently and efficiently estimate linear, mean-square continuous functionals of a high dimensional, approximately sparse regression. Such objects include a wide variety of interesting parameters such as the covariance between two regression...

Persistent link: https://www.econbiz.de/10012667932

Double machine learning for treatment and causal parameters

Chernozhukov, Victor; Chetverikov, Denis; Demirer, Mert; … - 2016

random forests, lasso, ridge, deep neural nets, boosted trees, as well as various hybrids and aggregates of these methods (e ….g. a hybrid of a random forest and lasso). We illustrate the application of the general theory through application to the …

Persistent link: https://www.econbiz.de/10011594359