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Generic machine learning inference on heterogenous treatment effects in randomized experiments

Chernozhukov, Victor; Demirer, Mert; Duflo, Esther; … - 2017

We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups, and average characteristics of most...

Persistent link: https://www.econbiz.de/10011775335

Finite-Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness

Armstrong, Timothy - 2018

) under the assumption of normal errors when the conditional mean of the outcome variable is constrained only by nonparametric … smoothness and/or shape restrictions. When the conditional mean is restricted to be Lipschitz with a large enough bound on the …

Persistent link: https://www.econbiz.de/10012931665

Finite-Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness

Armstrong, Timothy - 2019

mean of the outcome variable, we derive estimators and conﬁdence intervals (CIs) that are optimal inﬁnite samples when the … conditional mean. We also derive the minimum smoothness conditions on the conditional mean that are necessary for √n …-inference. When the conditional mean is restricted to be Lipschitz with a large enough bound on the Lipschitz constant, the optimal …

Persistent link: https://www.econbiz.de/10012895669

Robust estimation and inference in panels with interactive fixed effects

Armstrong, Timothy B.; Weidner, Martin; Zeleneev, Andrei - 2023

We consider estimation and inference for a regression coefficient in panels with interactive fixed effects (i.e., with a factor structure). We show that previously developed estimators and confidence intervals (CIs) might be heavily biased and size-distorted when some of the factors are weak. We...

Persistent link: https://www.econbiz.de/10014312069

Optimal two-sided tests for instrumental variables regression with heteroskedastic and autocorrelated errors

Moreira, Humberto; Moreira, Marcelo J. - 2016 - This version: June 10, 2016

This paper considers two-sided tests for the parameter of an endogenous variable in an instrumental variable (IV) model with heteroskedastic and autocorrelated errors. We develop the finite-sample theory of weighted-average power (WAP) tests with normal errors and a known long-run variance. We...

Persistent link: https://www.econbiz.de/10011485564

A critical value function approach, with an application to persistent time-series

Moreira, Marcelo J.; Mourão, Rafael; Moreira, Humberto - 2016

Researchers often rely on the t-statistic to make inference on parameters in statistical models. It is common practice to obtain critical values by simulation techniques. This paper proposes a novel numerical method to obtain an approximately similar test. This test rejects the null hypothesis...

Persistent link: https://www.econbiz.de/10011485576

Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings

Chernozhukov, Victor; Chetverikov, Denis; Kato, Kengo - 2016

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 bootstrap processes. The bounds of these approximations are non-asymptotic, which allows us to work with classes of...

Persistent link: https://www.econbiz.de/10011524717

Central limit theorems and bootstrap in high dimensions

Chernozhukov, Victor; Chetverikov, Denis; Kato, Kengo - 2016 - This version: May 31, 2016

In this paper, we derive central limit and bootstrap theorems for probabilities that centered high-dimensional vector sums hit rectangles and sparsely convex sets. Specifically, we derive Gaussian and bootstrap approximations for the probabilities that a root-n rescaled sample average of Xi is...

Persistent link: https://www.econbiz.de/10011525777

Testing many moment inequalities

Chernozhukov, Victor; Chetverikov, Denis; Kato, Kengo - 2016 - This version: September 10, 2015

This paper considers the problem of testing many moment inequalities where the number of moment inequalities, denoted by p, is possibly much larger than the sample size n. There are variety of economic applications where the problem of testing many moment in- equalities appears; a notable...

Persistent link: https://www.econbiz.de/10011525823

Conditional quantile processes based on series or many regressors

Belloni, Alexandre; Chernozhukov, Victor; Chetverikov, Denis - 2016 - This version of August 28, 2016

Quantile regression (QR) is a principal regression method for analyzing the impact of covariates on outcomes. The impact is described by the conditional quantile function and its functionals. In this paper we develop the nonparametric QR-series framework, covering many regressors as a special...

Persistent link: https://www.econbiz.de/10011525883

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