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Journal articles (2023), Journal of Econometrics, 236 (1), pp. 105467

We modeled long memory with just one lag!

BAUWENS Luc, CHEVILLON Guillaume , LAURENT Sebastien

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. Link to the article

BAUWENS, L., CHEVILLON, G. and LAURENT, S. (2023). We modeled long memory with just one lag! Journal of Econometrics, 236(1), pp. 105467.

Keywords : #Bayesian-estimation, #Ridge-regression, #Vector-autoregressive-model, #Forecasting