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We modeled long memory with just one lag!

Author

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  • Bauwens, Luc

    (Université catholique de Louvain, LIDAM/CORE, Belgium)

  • Chevillon, Guillaume
  • Laurent, Sébastien

Abstract

We build on two contributions that have found conditions for large dimensional networks or systems to generate long memory in their individual components, and provide a 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 US company stocks, as well as seasonally adjusted monthly streamflow series recorded at 97 locations of the Columbia river basin.

Suggested Citation

  • Bauwens, Luc & Chevillon, Guillaume & Laurent, Sébastien, 2022. "We modeled long memory with just one lag!," LIDAM Discussion Papers CORE 2022016, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:2022016
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    Cited by:

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    2. Jie Wang & Yongqiao Wang, 2025. "Forecasting Expected Shortfall and Value‐at‐Risk With Cross‐Sectional Aggregation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 391-423, March.
    3. Anna Mikusheva & Mikkel Sølvsten, 2025. "Linear regression with weak exogeneity," Quantitative Economics, Econometric Society, vol. 16(2), pages 367-403, May.
    4. Castro, Tomas del Barrio & Escribano, Alvaro & Sibbertsen, Philipp, 2025. "Modeling and forecasting the long memory of Cyclical Trends in paleoclimate data," Energy Economics, Elsevier, vol. 147(C).
    5. Shikta Singh & Supun Chandrasena & Yue Shi & Abdullah Alhussaini & Claude Diebolt & Martin Enilov & Tapas Mishra, 2026. "A Learning Model with Memory in the Financial Markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 31(1), pages 1203-1213, January.

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    Keywords

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    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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