Lasso Maximum Likelihood Estimation of Parametric Models with Singular Information Matrices

Fei Jin and Lung-Fei Lee
Additional contact information
Fei Jin: School of Economics, Shanghai University of Finance and Economics, Shanghai 200433, China

Econometrics, 2018, vol. 6, issue 1, 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 n -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 n -rate of convergence.

Keywords: penalized maximum likelihood; singular information matrix; lasso; oracle properties (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2225-1146/6/1/8/pdf (application/pdf)
https://www.mdpi.com/2225-1146/6/1/8/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:6:y:2018:i:1:p:8-:d:132670

Access Statistics for this article

Econometrics is currently edited by Ms. Jasmine Liu

More articles in Econometrics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().