Random autoregressive models: A structured overview

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.

Automated summary

Models characterized by autoregressive structure and random coefficients are potent tools for analyzing high-frequency, high-dimensional, and volatile time series data. However, the literature on such models is vast, sector-specific, and convoluted. Most models concentrate on individual data properties, yet combining various models and their sources of heterogeneity can yield greater benefits.