Testing for Shifts in Trend With an Integrated or Stationary Noise Component
提出一种检验时间序列趋势函数结构变化的方法，无需事先知道噪声是平稳还是单整，通过超有效估计自回归参数，使检验在两种噪声下具有相近的统计性质，并改进了小样本表现。
We consider testing for structural changes in the trend function of a time series without any prior knowledge of whether the noise component is stationary or integrated. Following Perron and Yabu (2009), we consider a quasi-feasible generalized least squares procedure that uses a super-efficient estimate of the sum of the autoregressive parameters αwhen α=1. This allows tests of basically the same size with stationary or integrated noise regardless of whether the break is known or unknown, provided that the Exp functional of Andrews and Ploberger (1994) is used in the latter case. To improve the finite-sample properties, we use the bias-corrected version of the estimate of αproposed by Roy and Fuller (2001). Our procedure has a power function close to that attainable if we knew the true value of αin many cases. We also discuss the extension to the case of multiple breaks.