centre for microdata methods and practice

ESRC centre

cemmap is an ESRC research centre

ESRC

Keep in touch

Subscribe to cemmap news

Inference on average treatment effects in aggregate panel data settings

Authors: Victor Chernozhukov , Kaspar W├╝thrich and Yinchu Zhu
Date: 12 June 2019
Type: cemmap Working Paper, CWP32/19
DOI: 10.1920/wp.cem.2019.3219

Abstract

This paper studies inference on treatment effects in aggregate panel data settings with a single treated unit and many control units. We propose new methods for making inference on average treatment effects in settings where both the number of pre-treatment and the number of post-treatment periods are large. We use linear models to approximate the counterfactual mean outcomes in the absence of the treatment. The counterfactuals are estimated using constrained Lasso, an essentially tuning free regression approach that nests difference-in-differences and synthetic control as special cases. We propose a K-fold cross-fitting procedure to remove the bias induced by regularization. To avoid the estimation of the long run variance, we construct a self-normalized t-statistic. The test statistic has an asymptotically pivotal distribution (a student t-distribution with K − 1 degrees of freedom), which makes our procedure very easy to implement. Our approach has several theoretical advantages. First, it does not rely on any sparsity assumptions. Second, it is fully robust against misspecification of the linear model. Third, it is more efficient than difference-in-means and difference-in-differences estimators. The proposed method demonstrates an excellent performance in simulation experiments, and is taken to a data application, where we re-evaluate the economic consequences of terrorism.

Download full version

Search cemmap

Search by title, topic or name.

Contact cemmap

Centre for Microdata Methods and Practice

How to find us

Tel: +44 (0)20 7291 4800

E-mail us