A NONPARAMETRIC GOODNESS-OF-FIT-BASED TEST FOR CONDITIONAL HETEROSKEDASTICITY
提出一种基于非参数拟合优度R²的条件异方差检验，通过局部多项式回归残差构造统计量，证明其渐近正态性和一致性，并给出自助法p值。
In this paper we propose a new nonparametric test for conditional heteroskedasticity based on a measure of nonparametric goodness-of-fit (R 2 ) that is obtained from the local polynomial regression of the residuals from a parametric regression on some covariates. We show that after being appropriately standardized, the nonparametric R 2 is asymptotically normally distributed under the null hypothesis and a sequence of Pitman local alternatives. We also prove the consistency of the test and propose a bootstrap method to obtain the bootstrap p -values. We conduct a small set of simulations and compare our test with some popular parametric and nonparametric tests in the literature.