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Lasso Maximum Likelihood Estimation of Parametric Models with Singular Information Matrices

Author

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  • Fei Jin

    (School of Economics, Shanghai University of Finance and Economics, Shanghai 200433, China
    Key Laboratory of Mathematical Economics (SUFE), Ministry of Education, Shanghai 200433, China)

  • Lung-fei Lee

    (Department of Economics, The Ohio State University, Columbus, OH 43210, USA)

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.

Suggested Citation

  • Fei Jin & Lung-fei Lee, 2018. "Lasso Maximum Likelihood Estimation of Parametric Models with Singular Information Matrices," Econometrics, MDPI, vol. 6(1), pages 1-24, February.
    • Handle: RePEc:gam:jecnmx:v:6:y:2018:i:1:p:8-:d:132670
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    References listed on IDEAS

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    Cited by:

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    3. George Karabatsos, 2023. "Approximate Bayesian computation using asymptotically normal point estimates," Computational Statistics, Springer, vol. 38(2), pages 531-568, June.

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