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首页> 外文期刊>Stochastic Processes and Their Applications: An Official Journal of the Bernoulli Society for Mathematical Statistics and Probability >Ergodicity of observation-driven time series models and consistency of the maximum likelihood estimator
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Ergodicity of observation-driven time series models and consistency of the maximum likelihood estimator

机译:观测驱动时间序列模型的遍历性和最大似然估计的一致性

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摘要

This paper deals with a general class of observation-driven time series models with a special focus on time series of counts. We provide conditions under which there exist strict-sense stationary and ergodic versions of such processes. The consistency of the maximum likelihood estimators is then derived for wellspecified and misspecified models.
机译:本文讨论了一般类别的观察驱动时间序列模型,特别关注计数的时间序列。我们提供了此类过程存在严格意义上的平稳和遍历版本的条件。然后,针对明确指定和错误指定的模型得出最大似然估计量的一致性。

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