Autoregressive Process Modeling via the Lasso Procedure

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dc.creatorNardi, Yuval
dc.creatorRinaldo, Alessandro
dc.date2008-05-08
dc.date.accessioned2026-07-07T09:37:45Z
dc.date.available2026-07-07T09:37:45Z
dc.descriptionThe Lasso is a popular model selection and estimation procedure for linear models that enjoys nice theoretical properties. In this paper, we study the Lasso estimator for fitting autoregressive time series models. We adopt a double asymptotic framework where the maximal lag may increase with the sample size. We derive theoretical results establishing various types of consistency. In particular, we derive conditions under which the Lasso estimator for the autoregressive coefficients is model selection consistent, estimation consistent and prediction consistent. Simulation study results are reported.
dc.description20 pages, 5 figures
dc.identifierhttps://arxiv.org/abs/0805.1179
dc.identifierhttp://arxiv.org/abs/0805.1179
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/160570
dc.subjectStatistics Theory
dc.titleAutoregressive Process Modeling via the Lasso Procedure
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