We modeled long memory with just one lag!

Luc Bauwens, Guillaume Chevillon and Sébastien Laurent

No 2022016, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

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.

Keywords: Bayesian estimation; Ridge regression; Vector autoregressive model; Forecasting (search for similar items in EconPapers)
Pages: 43
Date: 2022-04-03
New Economics Papers: this item is included in nep-ecm and nep-ets
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Related works:
Journal Article: We modeled long memory with just one lag! (2023)
Working Paper: We modeled long memory with just one lag! (2023)
Working Paper: We modeled long memory with just one lag! (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvco:2022016

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