  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.
