We propose a sequential monitoring scheme to find structural breaks in dynamic linear models. The monitoring scheme is based on a detector and a suitably chosen boundary function. If the detector crosses the boundary function, a structural break is detected. We provide the asymptotics for the procedure under the null hypothesis of stability. The consistency of the procedure is also proved. We derive the asymptotic distribution of the stopping time under the change point alternative. Monte Carlo simulation is used to show the size and the power of our method under several conditions. As an example, we study the real estate markets in Boston and Los Angeles, and at the national U.S. level. We find structural breaks in the markets, and we segment the data into stationary segments. It is observed that the autoregressive parameter is increasing but stays below 1.

我们提出了一种顺序监测方案来发现动态线性模型中的结构中断。The monitoring scheme is based on a detector and a suitably chosen boundary function. 如果检测器越过边界函数，则检测到结构断裂。我们在稳定性的零假设下为过程提供渐近线。也证明了程序的一致性。我们推导出变点方案下停止时间的渐近分布。蒙特卡罗模拟用于显示我们方法在多种条件下的大小和功效。例如，我们研究了波士顿和洛杉矶的房地产市场，以及美国全国的房地产市场。我们在市场中发现结构性中断，并将数据分割成固定的部分。