Mixing properties of non‐stationary multi‐variate count processes
研究了两种整数值过程的多变量版本，在外生协变量过程引入非平稳性下，证明了计数过程具有指数衰减系数的绝对正则性，并展示了该结果如何用于证明模型参数最小二乘估计的渐近正态性。
We consider multi‐variate versions of two popular classes of integer‐valued processes. While the transition mechanism is time‐homogeneous, a possible non‐stationarity is introduced by an exogeneous covariate process. We prove absolute regularity (‐mixing) for the count process with exponentially decaying mixing coefficients. The proof of this result makes use of some sort of contraction in the transition mechanism which allows a coupling of two versions of the count process such that they eventually coalesce. We show how this result can be used to prove asymptotic normality of a least squares estimator of an involved model parameter.