Journal
ECONOMETRICS
Volume 6, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/econometrics6010008
Keywords
penalized maximum likelihood; singular information matrix; lasso; oracle properties
Categories
Funding
- National Natural Science Foundation of China [71501119]
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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 root 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 root n-rate of convergence.
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