ASYMPTOTIC PROPERTIES OF THE GAUGE AND POWER OF STEP-INDICATOR SATURATION
研究了逐步指示变量饱和（SIS）方法在时间序列结构断点检测中的渐近理论，包括误检率的一致性和渐近正态性，并比较了其与Andrews断点检验的局部功效，发现SIS在断点靠近样本末端或间隔较近时表现更优。
Detecting multiple structural breaks at unknown dates is a central challenge in time-series econometrics. Step-indicator saturation (SIS) addresses this challenge during model selection, and we develop its asymptotic theory for tuning parameter choice. We study its frequency gauge—the false detection rate—and show it is consistent and asymptotically normal. Simulations suggest that a smaller gauge minimizes bias in post-selection regression estimates. For the small gauge situation, we develop a complementary Poisson theory. We compare the local power of SIS to detect shifts with that of Andrews’ break test. We find that SIS excels when breaks are near the sample end or closely spaced. An application to U.K. labor productivity reveals a growth slowdown after the 2008 financial crisis.