The editors of The Econometrics Journal decided that the 2020 Denis Sargan Econometrics Prize will be awarded to Neng-Chieh Chang (UCLA) for his article "Double/debiased machine learning for difference-in-differences models” in the May 2020 issue of The Econometrics Journal (https://doi.org/10.1093/ectj/utaa001).
Neng-Chieh's prize winning article provides an orthogonal extension of Abadie's (2005) semiparametric difference-in-differences (DiD) estimator. DiD is a popular strategy for estimating the effects of policy interventions and other treatments in economics. It requires a parallel trends assumption that may be violated if outcome dynamics differ between treated and control units. The semiparametric DiD estimator is a two-stage procedure that flexibly controls for such variation in outcome dynamics with observed characteristics. Neng-Chieh's article provides an orthogonal extension of this estimator that allows for the use of modern machine learning methods in its first stage. This is important in applications with high-dimensional data and, in particular, many possible control variables relative to the sample size. The article develops this "double/debiased machine learning DiD" (DMLDiD) estimator for various data structures, derives its asymptotic properties, demonstrates its good finite sample performance (relative to a DiD estimator that more naively employs machine learning), and illustrates its use in an empirical analysis of the effects of trade tariffs on corruption.
The 2020 Denis Sargan Econometrics Prize will be presented to Neng-Chieh Chang at the start of Serena Ng's Sargan lecture at the 2022 Royal Economic Society Conference, which is held online from 11-13 April 2022.
The Denis Sargan Econometrics Prize was introduced in 2011 by The Econometrics Journal on behalf of the Royal Economic Society. It is awarded for the best (unsolicited) article published in The Econometrics Journal in a given year by anyone who is within five years of receiving their doctorate. An honorarium of £1000 is awarded to the winning author.
