CONSISTENT AND CONSERVATIVE MODEL SELECTION WITH THE ADAPTIVE LASSO IN STATIONARY AND NONSTATIONARY AUTOREGRESSIONS

DOI10.1017/S0266466615000304zbMath1441.62778OpenAlexW2231447488MaRDI QIDQ2786685

Anders Bredahl Kock

Publication date: 23 February 2016

Published in: Econometric Theory (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1017/s0266466615000304

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

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Cites Work

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• On asymptotically optimal confidence regions and tests for high-dimensional models
• The Adaptive Lasso and Its Oracle Properties
• Oracle inequalities for high dimensional vector autoregressions
• Subset selection for vector autoregressive processes via adaptive Lasso
• On the distribution of the adaptive LASSO estimator
• Asymptotic properties of bridge estimators in sparse high-dimensional regression models
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• Towards a unified asymptotic theory for autoregression
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power
• Empirical Limits for Time Series Econometric Models

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