On the Oracle Property of the Adaptive Lasso in Stationary and Nonstationary Autoregressions
We show that the Adaptive LASSO is oracle efficient in stationary and non-stationary autoregressions. This means that it estimates parameters consistently, selects the correct sparsity pattern, and estimates the coefficients belonging to the relevant variables at the same asymptotic efficiency as if only these had been included in the model from the outset. In particular this implies that it is able to discriminate between stationary and non-stationary autoregressions and it thereby constitutes an addition to the set of unit root tests. However, it is also shown that the Adaptive LASSO has no power against shrinking alternatives of the form c/T where c is a constant and T the sample size if it is tuned to perform consistent model selection. We show that if the Adaptive LASSO is tuned to performed conservative model selection it has power even against shrinking alternatives of this form. Monte Carlo experiments reveal that the Adaptive LASSO performs particularly well in the presence of a unit root while being at par with its competitors in the stationary setting.
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- Serena Ng & Pierre Perron, 2001.
"LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power,"
Econometric Society, vol. 69(6), pages 1519-1554, November.
- Serena Ng & Pierre Perron, 1997. "Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power," Boston College Working Papers in Economics 369, Boston College Department of Economics, revised 01 Sep 2000.
- Pötscher, Benedikt M. & Leeb, Hannes, 2009.
"On the distribution of penalized maximum likelihood estimators: The LASSO, SCAD, and thresholding,"
Journal of Multivariate Analysis,
Elsevier, vol. 100(9), pages 2065-2082, October.
- Pötscher, Benedikt M. & Leeb, Hannes, 2007. "On the distribution of penalized maximum likelihood estimators: The LASSO, SCAD, and thresholding," MPRA Paper 5615, University Library of Munich, Germany.
- Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911.
- Kock, Anders Bredahl, 2013.
"Oracle Efficient Variable Selection In Random And Fixed Effects Panel Data Models,"
Cambridge University Press, vol. 29(01), pages 115-152, February.
- Anders Bredahl Kock, 2010. "Oracle Efficient Variable Selection in Random and Fixed Effects Panel Data Models," CREATES Research Papers 2010-56, School of Economics and Management, University of Aarhus.
- Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
- Leeb, Hannes & Potscher, Benedikt M., 2008.
"Sparse estimators and the oracle property, or the return of Hodges' estimator,"
Journal of Econometrics,
Elsevier, vol. 142(1), pages 201-211, January.
- Hannes Leeb & Benedikt M. Poetscher, 2005. "Sparse Estimators and the Oracle Property, or the Return of Hodges' Estimator," Cowles Foundation Discussion Papers 1500, Cowles Foundation for Research in Economics, Yale University, revised Apr 2007.
- Leeb, Hannes & P tscher, Benedikt M., 2005. "Model Selection And Inference: Facts And Fiction," Econometric Theory, Cambridge University Press, vol. 21(01), pages 21-59, February.
- Hansheng Wang & Guodong Li & Chih-Ling Tsai, 2007. "Regression coefficient and autoregressive order shrinkage and selection via the lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(1), pages 63-78.
- Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
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