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Resampling-Based Maximum Likelihood Estimation

Author

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  • Takahiro ITO

    (Graduate School of International Cooperation Studies, Kobe University)

Abstract

This study develops a novel distribution-free maximum likelihood estimator and formulates it for linear and binary choice models. The estimator is consistent and asymptotically normally distributed (at the rate of N-1/2). Monte Carlo simulation results show that the estimator is strongly consistent and efficient. For the binary model, when the linear combination of regressors is leptokurtic, the efficiency loss of having no distribution assumption is virtually nonexistent, and the estimator is always superior to the probit and other semiparametric estimators. The results further show that the estimator performs exceedingly well in the presence of a typical perfect prediction problem.

Suggested Citation

  • Takahiro ITO, 2023. "Resampling-Based Maximum Likelihood Estimation," GSICS Working Paper Series 40, Graduate School of International Cooperation Studies, Kobe University.
    • Handle: RePEc:kcs:wpaper:40
    as

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    File URL: https://www.research.kobe-u.ac.jp/gsics-publication/gwps/2023-40.pdf
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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