COMMENTS ON PAIRWISE LIKELIHOOD IN TIME SERIES MODELS
COMMENTS ON PAIRWISE LIKELIHOOD IN TIME SERIES MODELS
复制标题
关于时间序列模型中成对似然的评论
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
C. Yau
中科院分区：
文献类型：
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作者：
R. Davis;C. Yau
本文关注的是线性时间序列模型的成对似然估计过程的渐近性质。后者包括 ARMA 以及分数积分 ARMA 过程，其中分数积分参数 d 0.25，成对似然估计量甚至不是渐近正态的。给出了在定义可能性时使用所有观察对和连续观察对之间的比较。我们还探索了成对似然在流行的计数时间序列非线性模型中的应用。在这种情况下，如果不借助基于模拟的程序，就无法计算基于整个数据集的可能性。另一方面，可以精确地数值计算成对似然。我们在这种情况下说明了成对似然的良好性能。
This note is concerned with the asymptotic properties of pairwise like- lihood estimation procedures for linear time series models. The latter includes ARMA as well as fractionally integrated ARMA processes, where the fractional integration parameter d 0.25, the pairwise likelihood estimator is not even asymptotically normal. A comparison between using all pairs and consecutive pairs of observations in defining the likelihood is given. We also explore the application of pairwise likelihood to a popular nonlinear model for time series of counts. In this case, the likelihood based on the entire data set cannot be computed without resorting to simulation-based procedures. On the other hand, it is possible to numerically compute the pairwise likelihood precisely. We illustrate the good performance of pairwise likelihood in this case.