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首页> 外文期刊>Journal of Time Series Analysis >NEGATIVE BINOMIAL QUASI-LIKELIHOOD INFERENCE FOR GENERAL INTEGER-VALUED TIME SERIES MODELS
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NEGATIVE BINOMIAL QUASI-LIKELIHOOD INFERENCE FOR GENERAL INTEGER-VALUED TIME SERIES MODELS

机译：一般整数值时间序列模型的负二项式拟似然推断

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摘要

Two negative binomial quasi-maximum likelihood estimates (NB-QMLEs) for a general class of count time series models are proposed. The first one is the profile NB-QMLE calculated while arbitrarily fixing the dispersion parameter of the negative binomial likelihood. The second one, termed two-stage NB-QMLE, consists of four stages estimating both conditional mean and dispersion parameters. It is shown that the two estimates are consistent and asymptotically Gaussian under mild conditions. Moreover, the two-stage NB-QMLE enjoys a certain asymptotic efficiency property provided that a negative binomial link function relating the conditional mean and conditional variance is specified. The proposed NB-QMLEs are compared with the Poisson QMLE asymptotically and in finite samples for various well-known particular classes of count time series models such as the Poisson and negative binomial integer-valued GARCH model and the INAR(1) model. Application to a real dataset is given.
机译：针对计数时​​间序列模型的一般类别，提出了两个负二项式准最大似然估计（NB-QMLE）。第一个是在任意固定负二项式似然的色散参数的同时计算出的轮廓NB-QMLE。第二个称为两阶段NB-QMLE，由四个阶段组成，分别估计条件均值和分散参数。结果表明，这两个估计在温和条件下是一致的，并且是渐近高斯的。此外，如果指定了与条件均值和条件方差相关的负二项式链接函数，则两阶段NB-QMLE具有一定的渐近效率属性。将拟议的NB-QMLE与Poisson QMLE渐近地比较，并在有限样本中比较各种著名的特定类别的计数时间序列模型，例如Poisson和负二项式整数值GARCH模型以及INAR（1）模型。给出了对真实数据集的应用。

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