Resampling-Based Maximum Likelihood Estimation

Takahiro Ito

No 40, GSICS Working Paper Series from 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.

Keywords: semiparametric estimator; distribution-free maximum likelihood estimation; Monte Carlo Resampling with Replacement; binary choice model; perfect prediction problem (search for similar items in EconPapers)
Pages: 36 pages
Date: 2023-03
New Economics Papers: this item is included in nep-dcm and nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:kcs:wpaper:40

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