Speaker：Song Xiaojun Time: 14:00pm, Thursday, July 9, 2020 Site：Tencent Meeting ID 673970210 Abstract: This paper proposes a new class of nonparametric tests for the correct specification of generalized propensity score models. The test procedure is based on two different projection arguments, which lead to test statistics with several appealing properties. They accommodate high-dimensional covariates; are asymptotically invariant to the estimation method used to estimate the nuisance parameters and do not requite estimators to be root-n asymptotically linear; are fully data-driven and do not require tuning parameters, can be written in closed-form, facilitating the implementation of an easy-to-use multiplier bootstrap procedure. We show that our proposed tests are able to detect a broad class of local alternatives converging to the null at the parametric rate. Monte Carlo simulation studies indicate that our double projected tests have much higher power than other tests available in the literature, highlighting their practical appeal. Introduction to the Speaker： Dr.Xiaojun Song is an Assistant Professor in Guanghua School of Management, Peking University. He has published several papers in top-field econometric journals, such as Journal of Econometrics, Journal of Business & Economic Statistics, Oxford Bulletin of Economics and Statistics, among others.