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A parametric time series model with covariates for integers in Z

机译:Z中具有整数协变量的参数时间序列模型

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摘要

While models for integer valued time series are now abundant, there is a shortage of similar models when the time series refer to data defined on Z, i.e., in both the positive and negative integers. Such data occur in certain disciplines and the need for such models also appear when taking differences of a positive integer count time series. In addition one would often like to include covariates to explain variations in the variable of interest. In this article we construct a model doing all these assuming a specific innovation distribution and provide fully parametric inference, including prediction. Real data applications on accidents and financial returns are given. Finally we also discuss alternative models and extensions.
机译:虽然整数值时间序列的模型现在很丰富,但是当时间序列引用在Z上定义的数据(即正整数和负整数)时,都缺少相似的模型。在某些学科中会出现此类数据,并且在采用正整数计数时间序列的差异时也会出现对此类模型的需求。另外,人们通常希望包括协变量来解释感兴趣变量的变化。在本文中,我们将假设所有特定创新分布而构建一个模型,并提供包括预测在内的完全参数推断。给出了有关事故和财务回报的实际数据应用程序。最后,我们还将讨论替代模型和扩展。

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