Department of Economics
Massachusetts Institute of Technology
77 Massachusetts Avenue E52-300
Cambridge, MA 02139
Institutional Affiliation: Massachusetts Institute of Technology
NBER Working Papers and Publications
|June 2018||Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments|
with Victor Chernozhukov, Esther Duflo, Iván Fernández-Val: w24678
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 and least impacted units. The approach is valid in high dimensional settings, where the effects are proxied by machine learning methods. We post-process these proxies into the estimates of the key features. Our approach is generic, it can be used in conjunction with penalized methods, deep and shallow neural networks, canonical and new random forests, boosted trees, and ensemble methods. It does not rely on strong assumptions. In particular, we don’t require conditions for consistency of the machine ...
|June 2017||Double/Debiased Machine Learning for Treatment and Structural Parameters|
with Victor Chernozhukov, Denis Chetverikov, 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...
Published: Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," The Econometrics Journal, vol 21(1), pages C1-C68. citation courtesy of
|February 2017||Estimating Global Bank Network Connectedness|
with Francis X. Diebold, Laura Liu, Kamil Yılmaz: w23140
We use LASSO methods to shrink, select and estimate the high-dimensional network linking the publicly-traded subset of the world's top 150 banks, 2003-2014. We characterize static network connectedness using full-sample estimation and dynamic network connectedness using rolling-window estimation. Statically, we find that global bank equity connectedness has a strong geographic component, whereas country sovereign bond connectedness does not. Dynamically, we find that equity connectedness increases during crises, with clear peaks during the Great Financial Crisis and each wave of the subsequent European Debt Crisis, and with movements coming mostly from changes in cross-country as opposed to within-country bank linkages.
Published: Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Estimating global bank network connectedness," Journal of Applied Econometrics, vol 33(1), pages 1-15. citation courtesy of