Copulas and Temporal Dependence
研究了用连接函数建模平稳马尔可夫链中潜在非线性时间依赖性的方法，给出了几何混合率的充分条件，并验证了常用参数连接函数是否满足这些条件。
An emerging literature in time series econometrics concerns the modeling of potentially nonlinear temporal dependence in stationary Markov chains using copula functions. We obtain sufficient conditions for a geometric rate of mixing in models of this kind. Geometric β-mixing is established under a rather strong sufficient condition that rules out asymmetry and tail dependence in the copula function. Geometric ρ-mixing is obtained under a weaker condition that permits both asymmetry and tail dependence. We verify one or both of these conditions for a range of parametric copula functions that are popular in applied work. Copyright 2010 The Econometric Society.