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We modeled long memory with just one lag!

Author

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  • Luc Bauwens

    (CORE - Center of Operation Research and Econometrics [Louvain] - UCL - Université Catholique de Louvain = Catholic University of Louvain)

  • Guillaume Chevillon

    (ESSEC Business School)

  • Sébastien Laurent

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique, AMU IAE - Institut d'Administration des Entreprises (IAE) - Aix-en-Provence - AMU - Aix Marseille Université)

Abstract

Two recent contributions have found conditions for large dimensional networks or systems to generate long memory in their individual components. We build on these and provide a multivariate methodology for modeling and forecasting series displaying long range dependence. We model long memory properties within a vector autoregressive system of order 1 and consider Bayesian estimation or ridge regression. For these, we derive a theory-driven parametric setting that informs a prior distribution or a shrinkage target. Our proposal significantly outperforms univariate time series long-memory models when forecasting a daily volatility measure for 250 U.S. company stocks over twelve years. This provides an empirical validation of the theoretical results showing long memory can be sourced to marginalization within a large dimensional system.

Suggested Citation

  • Luc Bauwens & Guillaume Chevillon & Sébastien Laurent, 2023. "We modeled long memory with just one lag!," Post-Print hal-04185755, HAL.
  • Handle: RePEc:hal:journl:hal-04185755
    DOI: 10.1016/j.jeconom.2023.04.010
    Note: View the original document on HAL open archive server: https://amu.hal.science/hal-04185755
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    Cited by:

    1. Barrio Castro, Tomás del & Escribano, Álvaro & Sibbertsen, Philipp, 2024. "Modeling and Forecasting the Long Memory of Cyclical Trends in Paleoclimate Data," UC3M Working papers. Economics 43987, Universidad Carlos III de Madrid. Departamento de Economía.
    2. Shikta Sing & Supun Chandrasena & Yue Shi & Abdullah Alhussain & Claude DIEBOLT & Martin Enilov & Tapas Mishra, 2024. "A Learning Model with Memory in the Financial Markets," Working Papers 06-24, Association Française de Cliométrie (AFC).
    3. Anna Mikusheva & Mikkel S{o}lvsten, 2023. "Linear Regression with Weak Exogeneity," Papers 2308.08958, arXiv.org, revised Jan 2024.

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    More about this item

    Keywords

    Bayesian estimation; Ridge regression; Vector autoregressive; model Forecasting;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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