USING A BIMODAL KERNEL FOR A NONPARAMETRIC REGRESSION SPECIFICATION TEST

作者：Park Cheolyong^*; Kim Tae Yoon; Ha Jeongcheol; Luo Zhi Ming; Hwang Sun Young

来源：Statistica Sinica, 2015, 25(3): 1145-1161.

DOI：10.5705/ss.2014.008

• bimodal kernel
• convergence rate change
• correlated error
• nonparametric specification test

For a nonparametric regression model with a fixed design, we consider the model specification test based on a kernel. We find that a bimodal kernel is useful for the model specification test with a correlated error, whereas a conventional unimodal kernel is useful only for an iid error. Another finding is that the model specification test suffers from a convergence rate change depending on whether the errors are correlated or not. These results are verified by deriving an asymptotic null distribution and asymptotic (local) power, and by performing a simulation. The validity of the bimodal kernel for testing is demonstrated with the "drum roller" data (see Laslett (1994) and Altman (1994)).

• 出版日期2015-7