DETECTION OF NONCONSTANT LONG MEMORY PARAMETER
研究了如何检测时间序列中长记忆参数是否发生变化，提出了基于前后样本方差比值的检验统计量，并通过模拟验证其优于KPSS检验。
This article deals with detection of a nonconstant long memory parameter in time series. The null hypothesis presumes stationary or nonstationary time series with a constant long memory parameter, typically an I ( d ) series with d > −.5 . The alternative corresponds to an increase in persistence and includes in particular an abrupt or gradual change from I ( d 1 ) to I ( d 2 ), −.5 < d 1 < d 2 . We discuss several test statistics based on the ratio of forward and backward sample variances of the partial sums. The consistency of the tests is proved under a very general setting. We also study the behavior of these test statistics for some models with a changing memory parameter. A simulation study shows that our testing procedures have good finite sample properties and turn out to be more powerful than the KPSS-based tests (see Kwiatkowski, Phillips, Schmidt and Shin, 1992) considered in some previous works.