We modeled long memory with just one lag!

Luc Bauwens, Guillaume Chevillon and Sébastien Laurent

Journal of Econometrics, 2023, vol. 236, issue 1

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)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: 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:eee:econom:v:236:y:2023:i:1:s0304407623001616

DOI: 10.1016/j.jeconom.2023.04.010

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