论文标题
随机自回归模型：结构化概述
Random autoregressive models: A structured overview
论文作者
论文摘要
以自回旋结构和随机系数为特征的模型是分析高频，高维和波动性时间序列的强大工具。有关此类模型的可用文献是广泛的，但也是部门，重叠且令人困惑的文献。大多数模型都集中在数据的一个属性上，而通过结合各种模型及其异质性来源的强度，可以获得很多。我们介绍了具有随机系数的自回归模型文献的结构化概述。我们描述了模型之间的层次结构和类比，并且我们系统地列出了属性，估计方法，测试，软件包和典型应用程序。
Models characterized by autoregressive structure and random coefficients are powerful tools for the analysis of high-frequency, high-dimensional and volatile time series. The available literature on such models is broad, but also sectorial, overlapping, and confusing. Most models focus on one property of the data, while much can be gained by combining the strength of various models and their sources of heterogeneity. We present a structured overview of the literature on autoregressive models with random coefficients. We describe hierarchy and analogies among models, and for each we systematically list properties, estimation methods, tests, software packages and typical applications.
