论文标题
包括非参数回归的测试
Encompassing Tests for Nonparametric Regressions
论文作者
论文摘要
我们设置了一个正式的框架，以通过L2距离来表征非参数模型的包含。我们将其与以前有关非参数回归模型比较的文献进行了对比。然后，我们为完全非参数的包含假设制定了测试程序。我们的测试统计数据取决于内核回归，从而提出了带宽的选择。我们研究了两种替代方法，以获得我们的测试统计数据的“小偏置属性”。我们显示了一种野生引导法的有效性。我们从经验上研究了数据驱动的带宽，并说明了我们对小样本和中等样本的测试的吸引力。
We set up a formal framework to characterize encompassing of nonparametric models through the L2 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.
