We modeled long memory with just one lag!

Luc Bauwens, Guillaume Chevillon and Sébastien Laurent

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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)
Date: 2023-09
Note: View the original document on HAL open archive server: https://amu.hal.science/hal-04185755
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Citations: View citations in EconPapers (3)

Published in Journal of Econometrics, 2023, 236 (1), pp.105467. ⟨10.1016/j.jeconom.2023.04.010⟩

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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:hal:journl:hal-04185755

DOI: 10.1016/j.jeconom.2023.04.010

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