演講者簡介 : Professor Rothstein received his Ph.D. in Economics from University of California, Berkeley in 2003. He is Chancellor's professor of public policy and economics at the University of California, Berkeley, with appointments in the Department of Economics and the Goldman School of Public Policy. He is also the co-founder and co-director, with Till von Wachter (UCLA), of the California Policy Lab. His research examines education policy, tax and transfer policy, and the labor market. His recent work includes studies of teacher quality, school finance, intergenerational economic mobility, take-up of safety net benefits, and the labor market during the Great Recession.
演講摘要 : The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The “synthetic control” is a weighted average of control units that balances the treated unit’s pretreatment outcomes and other covariates as closely as possible. A critical feature of the original proposal is to use SCM only when the fit on pretreatment outcomes is excellent. We propose Augmented SCM as an extension of SCM to settings where such pretreatment fit is infeasible. Analogous to bias correction for inexact matching, augmented SCM uses an outcome model to estimate the bias due to imperfect pretreatment fit and then de-biases the original SCM estimate. Our main proposal, which uses ridge regression as the outcome model, directly controls pretreatment fit while minimizing extrapolation from the convex hull. This estimator can also be expressed as a solution to a modified synthetic controls problem that allows negative weights on some donor units. We bound the estimation error of this approach under different data-generating processes, including a linear factor model, and show how regularization helps to avoid over-fitting to noise. We demonstrate gains from Augmented SCM with extensive simulation studies and apply this framework to estimate the impact of the 2012 Kansas tax cuts on economic growth. We implement the proposed method in the new augsynth R package.
