Random autoregressive models: A structured overview

Research output: Contribution to journalArticleAcademicpeer-review

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.

Original languageEnglish
JournalEconometric Reviews
VolumeXX
Issue numberXX
DOIs
Publication statusAccepted/In press - 2021

Bibliographical note

Publisher Copyright:
© 2021 The Author(s). Published with license by Taylor and Francis Group, LLC.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • (Generalized) Autoregressive conditional heteroskedasticity models
  • (Generalized) Random coefficient autoregressive models
  • Autoregressive panel data models
  • C22
  • C23
  • C24
  • C32
  • C33
  • Random coefficient panel models
  • Time-series-cross-section models

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