Abstract

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 sector-specific, 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.

摘要

以自回归结构和随机系数为特征的模型是分析高频、高维和易变时间序列的有力工具。关于此类模型的可用文献很广泛，但也有特定部门、重叠和混乱。大多数模型专注于数据的一个属性，而通过结合各种模型的优势及其异质性来源可以获得很多。我们对具有随机系数的自回归模型的文献进行了结构化概述。我们描述了模型之间的层次结构和类比，并系统地列出了每个模型的属性、估计方法、测试、软件包和典型应用。