ENCOMPASSING TESTS FOR NONPARAMETRIC REGRESSIONS
为比较非参数回归模型建立了基于L2距离的包容性框架，并开发了完全非参数的检验方法，通过核回归构造检验统计量，解决了带宽选择问题，并验证了wild bootstrap方法的有效性。
We set up a formal framework to characterize encompassing of nonparametric models through the $L^2$ distance. We contrast it to previous literature on the comparison of nonparametric regression models. We then develop testing procedures for the encompassing hypothesis that are fully nonparametric. Our test statistics depend on kernel regression, raising the issue of bandwidth’s choice. We investigate two alternative approaches to obtain a “small bias property” for our test statistics. We show the validity of a wild bootstrap method. We empirically study the use of a data-driven bandwidth and illustrate the attractive features of our tests for small and moderate samples.