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摘要
En 中文
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 beta-mixing is established under a rather strong sufficient condition that rules out asymmetry and tail dependence in the copula function. Geometric -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.
Keyword:
Copula
Markov chain
maximal correlation
mean square contingency
mixing
canonical correlation
tail dependence
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