Subset selection for vector autoregressive processes via adaptive Lasso

DOI10.1016/j.spl.2010.07.013zbMath1202.62122OpenAlexW2036612144MaRDI QIDQ613145

Yunwen Ren, Xin Sheng Zhang

Publication date: 20 December 2010

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2010.07.013

zbMATH Keywords

Bayesian information criterionoracle propertyadaptive Lassovector autoregressive processesHQ criterion

Mathematics Subject Classification ID

Related Items (10)

Space-time short- to medium-term wind speed forecasting ⋮ Iteratively reweighted adaptive Lasso for conditional heteroscedastic time series with applications to AR-ARCH type processes ⋮ Consistent model selection criteria and goodness-of-fit test for common time series models ⋮ Two-step adaptive model selection for vector autoregressive processes ⋮ Oracle M‐Estimation for Time Series Models ⋮ On a Semiparametric Data‐Driven Nonlinear Model with Penalized Spatio‐Temporal Lag Interactions ⋮ CONSISTENT AND CONSERVATIVE MODEL SELECTION WITH THE ADAPTIVE LASSO IN STATIONARY AND NONSTATIONARY AUTOREGRESSIONS ⋮ The Doubly Adaptive LASSO for Vector Autoregressive Models ⋮ A NEW APPROACH TO SELECT THE BEST SUBSET OF PREDICTORS IN LINEAR REGRESSION MODELLING: BI-OBJECTIVE MIXED INTEGER LINEAR PROGRAMMING ⋮ OPTIMAL MULTISTEP VAR FORECAST AVERAGING

Cites Work

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• The Adaptive Lasso and Its Oracle Properties
• Subset selection for vector autoregressive processes using Lasso
• Estimating the dimension of a model
• Asymptotics for Lasso-type estimators.
• Least angle regression. (With discussion)
• On the asymptotics of constrained \(M\)-estimation
• On the adaptive elastic net with a diverging number of parameters

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