A Simple Test for White Noise in Functional Time Series
提出一种基于谱密度算子L2距离的函数型时间序列白噪声检验方法，估计量渐近正态，无需估计长期方差或重抽样，还可检验相关偏离并构建置信区间。
We propose a new procedure for white noise testing of a functional time series. Our approach is based on an explicit representation of the L 2 ‐distance between the spectral density operator and its best ( L 2 ‐)approximation by a spectral density operator corresponding to a white noise process. The estimation of this distance can be easily accomplished by sums of periodogram kernels, and it is shown that an appropriately standardized version of the estimator is asymptotically normal distributed under the null hypothesis (of functional white noise) and under the alternative. As a consequence, we obtain a very simple test (using the quantiles of the normal distribution) for the hypothesis of a white noise functional process. In particular, the test does not require either the estimation of a long‐run variance (including a fourth order cumulant) or resampling procedures to calculate critical values. Moreover, in contrast to all other methods proposed in the literature, our approach also allows testing for ‘relevant’ deviations from white noise and constructing confidence intervals for a measure that measures the discrepancy of the underlying process from a functional white noise process.