Tests for Structural Changes in Time Series of Counts
针对离散取值的计数时间序列，提出了基于经验概率生成函数的L2型检验统计量，用于检测分布或参数的结构变化，并研究了渐近性质与蒙特卡洛功效。
Abstract We propose methods for detecting structural changes in time series with discrete‐valued observations. The detector statistics come in familiar L2‐type formulations incorporating the empirical probability generating function. Special emphasis is given to the popular models of integer autoregression and Poisson autoregression. For both models, we study mainly structural changes due to a change in distribution, but we also comment for the classical problem of parameter change. The asymptotic properties of the proposed test statistics are studied under the null hypothesis as well as under alternatives. A Monte Carlo power study on bootstrap versions of the new methods is also included along with a real data example.