文章基本信息

标题：Nonlinearity and Temporal Dependence
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作者：Chen, Xiaohong,Hansen ; Lars P.,Carrasco, Marine
期刊名称：COWLES Foundation Discussion Paper / Cowles Foundation for Research in Economics
出版年度：2008
卷号：1
出版社：Yale University
摘要：Nonlinearities in the drift and diffusion coefficients influence temporal dependence in scalar diffusion models. We study this link using two notions of temporal dependence: beta-mixing and rho-mixing. We show that beta-mixing and rho-mixing with exponential decay are essentially equivalent concepts for scalar diffusions. For stationary diffusions that fail to be rho-mixing, we show that they are still beta-mixing except that the decay rates are slower than exponential. For such processes we find transformations of the Markov states that have finite variances but infinite spectral densities at frequency zero. Some have spectral densities that diverge at frequency zero in a manner similar to that of stochastic processes with long memory. Finally we show how nonlinear, state-dependent, Poisson sampling alters the unconditional distribution as well as the temporal dependence.
关键词：Mixing, Diffusion, Strong dependence, Long memory, Poisson sampling