Nonparametric Tests of Linearity for Time Series
提出基于条件均值和条件方差非参数估计的时间序列线性性检验，通过重采样构造零分布，在实例中表现优于参数检验和双谱非参数检验。
We introduce tests of linearity for time series based on nonparametric estimates of the conditional mean and the conditional variance. The tests are compared to a number of parametric tests and to nonparametric tests based on the bispectrum. Asymptotic expressions give bad approximations, and the null distribution under linearity is constructed using resampling of the best linear approximation. The new tests perform well on the examples tested.