Regularized Estimation of High-Dimensional Vector AutoRegressions with Weakly Dependent Innovations

Ricardo P. Masini, Marcelo Medeiros () and Eduardo F. Mendes

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Abstract: There has been considerable advance in understanding the properties of sparse regularization procedures in high-dimensional models. In time series context, it is mostly restricted to Gaussian autoregressions or mixing sequences. We study oracle properties of LASSO estimation of weakly sparse vector-autoregressive models with heavy tailed, weakly dependent innovations with virtually no assumption on the conditional heteroskedasticity. In contrast to current literature, our innovation process satisfy an $L^1$ mixingale type condition on the centered conditional covariance matrices. This condition covers $L^1$-NED sequences and strong ($\alpha$-) mixing sequences as particular examples.

Date: 2019-12, Revised 2021-06
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Related works:
Journal Article: Regularized estimation of high‐dimensional vector autoregressions with weakly dependent innovations (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1912.09002

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