We modeled long memory with just one lag!
(2023) Journal of Econometrics — Vol. 236, n° 1, p. 105467 (2023)
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- Authors
Bauwens, Luc 
- 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.
- Affiliations
Citations
Bauwens, L., Chevillon, G., & Laurent, S. (2023). We modeled long memory with just one lag! Journal of Econometrics, 236(1), 105467. https://doi.org/10.1016/j.jeconom.2023.04.010 (Original work published 2023)