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Random autoregressive models: A structured overview

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  • Marta Regis
  • Paulo Serra
  • Edwin R. van den Heuvel

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.

Suggested Citation

  • Marta Regis & Paulo Serra & Edwin R. van den Heuvel, 2022. "Random autoregressive models: A structured overview," Econometric Reviews, Taylor & Francis Journals, vol. 41(2), pages 207-230, February.
    • Handle: RePEc:taf:emetrv:v:41:y:2022:i:2:p:207-230
    DOI: 10.1080/07474938.2021.1899504
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    Cited by:

    1. Aknouche, Abdelhakim & Almohaimeed, Bader & Dimitrakopoulos, Stefanos, 2024. "Noising the GARCH volatility: A random coefficient GARCH model," MPRA Paper 120456, University Library of Munich, Germany, revised 15 Mar 2024.
    2. Aknouche, Abdelhakim & Gouveia, Sonia & Scotto, Manuel, 2023. "Random multiplication versus random sum: auto-regressive-like models with integer-valued random inputs," MPRA Paper 119518, University Library of Munich, Germany, revised 18 Dec 2023.
    3. Christian Di Pietro & Mariafortuna Pietroluongo & Marco M. Sorge, 2023. "Stochastic Ordering of Stationary Distributions of Linear Recurrences: Further Results and Economic Applications," Economies, MDPI, vol. 11(4), pages 1-10, April.

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