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• W2996563449 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." @default.
• W2996563449 created "2019-12-26" @default.
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• W2996563449 date "2019-12-19" @default.
• W2996563449 modified "2023-09-23" @default.
• W2996563449 title "Regularized Estimation of High-Dimensional Vector AutoRegressions with Weakly Dependent Innovations" @default.
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