A non‐parametric test for multi‐variate trend functions
提出一种稳健的非参数检验方法，用于判断多元时间序列中参数趋势函数设定是否正确，能处理序列相关、截面依赖和时变方差，并通过模拟验证了有限样本表现。
We propose a consistent non‐parametric test for the correct specification of parametric trend functions in multi‐variate time series. The new test takes the form of the U ‐statistic and is robust to serial and cross‐sectional dependence and time‐varying variances in error terms. The test statistic is shown to have a limiting standard normal distribution under the null and diverge to infinity under the alternative. Thus the test is consistent against any fixed alternative. The test is also shown to have non‐trivial asymptotic power against two classes of local alternatives approaching the null at different rates. A set of simulations is conducted to evaluate the finite‐sample performance of the test.