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Vanderbilt University 李彤教授学术报告

作者:    来源:    日期:2018-07-13

   

    【报告题目】:  Treatment Effects in Difference in Differences Models with Panel Data

 

  【主讲人】: 李彤教授

 

  【报告时间】:2018年07月13日上午10:00-11:30

 

  【报告地点】:北京理工大学 主楼 六层 

 

  【主讲人简介】:

  Tong Li is Gertrude Conaway Vanderbilt Professor of Economics at the Department of Economics, Vanderbilt University. His primary research and teaching interests are microeconometrics with a focus on identification and inference of econometric models with latent variables, and game-theoretic models.  He also studies dynamic/nonlinear panel data models, and empirical microeconomics with a focus on empirical analysis of strategic behavior of agents with asymmetric information. His research has been supported by the National Science Foundation and the American Statistical Association Committee on Law and Justice Statistics. He has served as an associate editor of the Journal of Econometrics, the Journal of Applied Econometrics, the Journal of Econometric Methods, and the Journal of Economic Behavior and Organization, and he is currently serving as Co-Editor of the Journal of Econometric Methods. Since 1999 he has supervised thirteen Ph.D. dissertations and has placed students on faculty at London School of Economics, National Chi-Nan University, North Carolina State University, National University of Singapore, Queens University, Remin University of China, Shanghai Jiao Tong University, Temple University, among others.

 

 

   内容简介】:

  This paper considers identification and estimation of the Quantile Treatment Effect on the Treated (QTT) under a straightforward distributional extension of the most commonly invoked Mean Difference in Differences assumption used for identifying the Average Treatment Effect on the Treated (ATT). Identification of the QTT is more complicated than the ATT though because it depends on the unknown dependence between the change in untreated potential outcomes and the initial level of untreated potential outcomes for the treated group. To address this issue, we introduce a new Copula Stability Assumption that says that the missing dependence is constant over time. Under this assumption and when panel data is available, the missing dependence can be recovered, and the QTT is identified. Second, we allow for identification to hold only after conditioning on covariates and provide very simple estimators based on propensity score re-weighting for this case. We use our method to estimate the effect of increasing the minimum wage on quantiles of local labor markets' unemployment rates and find significant heterogeneity.