Testing for a Shift in Mean Without Having to Estimate Serial-Correlation Parameters
提出一种检验单变量时间序列均值突变的方法，无需估计序列相关参数，适用于误差平稳或存在单位根的情况，突变时间已知或未知均可。
Abstract Tests for detecting a shift in the mean of a univariate time series that do not require estimation of serial-correlation parameters are proposed. The statistics are valid whether the errors are stationary or have a unit root. The date of the shift may be known or unknown. The statistics are based on a simple transformation of the data and are functions of partial sums of the data. These so-called partial sum statistics are shown to be asymptotically invariant to serial-correlation parameters. The statistics are shown to have good size and power properties asymptotically and in finite samples. KEY WORDS: HACEPartial sumStructural changeUnit rootWald test