报告地点：维格堂319

报告摘要： We propose nonparametric tests for the equality of two conditional distributions. To avoid the estimation of conditional density functions, we transform the null hypothesis into an equivalent characterization that a function involving only unconditional expectations equals zero everywhere. Based on an empirical analog of this function, which is $\sqrt{N}$-consistent and converges weakly to a Gaussian limit, we construct the Kolmogorov-Smirnov (KS) and Cram\'{e}r-von Mises (CvM) statistics. The critical values are computed by a multiplier bootstrap procedure. The proposed KS and CvM tests are proved to be asymptotically size-controlled and consistent against any fixed alternative, and we also study the local power. Monte Carlo experiments illustrate good performances of these tests in finite samples.