Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/195445
Authors: 
Jin, Fei
Lee, Lung-Fei
Year of Publication: 
2018
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 6 [Year:] 2018 [Issue:] 1 [Pages:] 1-24
Abstract: 
An information matrix of a parametric model being singular at a certain true value of a parameter vector is irregular. The maximum likelihood estimator in the irregular case usually has a rate of convergence slower than the Ín-rate in a regular case. We propose to estimate such models by the adaptive lasso maximum likelihood and propose an information criterion to select the involved tuning parameter. We show that the penalized maximum likelihood estimator has the oracle properties. The method can implement model selection and estimation simultaneously and the estimator always has the usual Ín-rate of convergence.
Subjects: 
penalized maximum likelihood
singular information matrix
lasso
oracle properties
JEL: 
C13
C18
C51
C52
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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