We modeled long memory with just one lag!

Luc Bauwens, Guillaume Chevillon and Sébastien Laurent
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Sébastien Laurent: Aix-Marseille University

No 3234, LIDAM Reprints CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

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.

Keywords: Bayesian estimation; Ridge regression; Vector autoregressive model; Forecasting (search for similar items in EconPapers)
JEL-codes: C10 C32 C58 (search for similar items in EconPapers)
Pages: 21
Date: 2023-04-19
Note: In: Journal of Econometrics, 2023, vol. 236(1), 105467
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Citations: View citations in EconPapers (1)

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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! (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvrp:3234

DOI: 10.1016/j.jeconom.2023.04.010

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