Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/249728 
Year of Publication: 
2020
Series/Report no.: 
Texto para discussão No. 680
Publisher: 
Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Departamento de Economia, Rio de Janeiro
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 L1 mixingale type condition on the centered conditional covariance matrices. This condition covers L1-NED sequences and strong (ff-) mixing sequences as particular examples. From a modeling perspective, it covers several multivariate-GARCH specifications, such as the BEKK model, and other factor stochastic volatility specifications that were ruled out by assumption in previous studies.
Subjects: 
high-dimensional time series
LASSO
VAR
mixing
JEL: 
C32
C55
C58
Document Type: 
Working Paper

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