论文标题
单位 - 韦布尔自回旋运动平均模型
Unit-Weibull Autoregressive Moving Average Models
论文作者
论文摘要
在这项工作中，我们介绍了连续随机变量的单位 - 韦布尔自动回归移动平均模型，以$（0,1）$为单位。提出的模型是一个观察驱动的模型，在一组协变量和过程的历史上有条件地，假定随机分量遵循通过其$ρ$ th ventile参数进行参数的单位weibull分布。系统的组件规定了类似ARMA的结构，以通过链接对条件的$ρ$ TH对有条件的$ρ$进行建模。建议模型中的参数估计是使用部分最大似然进行的，为此，我们为评分矢量和部分信息矩阵提供了封闭的公式。我们还讨论了一些推论工具，例如置信区间的构建，假设测试，模型选择和预测。进行了一项蒙特卡洛模拟研究，以评估所提出的部分最大似然方法的有限样本性能。最后，我们通过使用美国的制造能力利用来将方法与文献中的其他方法进行对比，从而研究了预测能力。
In this work we introduce the class of unit-Weibull Autoregressive Moving Average models for continuous random variables taking values in $(0,1)$. The proposed model is an observation driven one, for which, conditionally on a set of covariates and the process' history, the random component is assumed to follow a unit-Weibull distribution parameterized through its $ρ$th quantile. The systematic component prescribes an ARMA-like structure to model the conditional $ρ$th quantile by means of a link. Parameter estimation in the proposed model is performed using partial maximum likelihood, for which we provide closed formulas for the score vector and partial information matrix. We also discuss some inferential tools, such as the construction of confidence intervals, hypotheses testing, model selection, and forecasting. A Monte Carlo simulation study is conducted to assess the finite sample performance of the proposed partial maximum likelihood approach. Finally, we examine the prediction power by contrasting our method with others in the literature using the Manufacturing Capacity Utilization from the US.
