NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH

Denis Chetverikov

UCLA, Department of Economics
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Bunche Hall, Room 8283
Los Angeles, CA 90095-1477

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NBER Working Papers and Publications

June 2017Double/Debiased Machine Learning for Treatment and Structural Parameters
with Victor Chernozhukov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, James Robins: w23564
We revisit the classic semiparametric problem of inference on a low dimensional parameter θ_0 in the presence of high-dimensional nuisance parameters η_0. We depart from the classical setting by allowing for η_0 to be so high-dimensional that the traditional assumptions, such as Donsker properties, that limit complexity of the parameter space for this object break down. To estimate η_0, we consider the use of statistical or machine learning (ML) methods which are particularly well-suited to estimation in modern, very high-dimensional cases. ML methods perform well by employing regularization to reduce variance and trading off regularization bias with overfitting in practice. However, both regularization bias and overfitting in estimating η_0 cause a heavy bias in estimators of θ_0 that are...
March 2015IV Quantile Regression for Group-level Treatments, with an Application to the Distributional Effects of Trade
with Bradley Larsen, Christopher Palmer: w21033
We present a methodology for estimating the distributional effects of an endogenous treatment that varies at the group level when there are group-level unobservables, a quantile extension of Hausman and Taylor (1981). Because of the presence of group-level unobservables, standard quantile regression techniques are inconsistent in our setting even if the treatment is independent of unobservables. In contrast, our estimation technique is consistent as well as computationally simple, consisting of group-by-group quantile regression followed by two-stage least squares. Using the Bahadur representation of quantile estimators, we derive weak conditions on the growth of the number of observations per group that are sufficient for consistency and asymptotic zero-mean normality of our estimator. As...

Published: Econometrica, 2016, 84(2), 809-833.

 
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