Arbeitspapier

Comparison of parametric and semi-parametric binary response models

A Bayesian semi-parametric estimation of the binary response model using Markov Chain Monte Carlo algorithms is proposed. The performances of the parametric and semi-parametric models are presented. The mean squared errors, receiver operating characteristic curve, and the marginal effect are used as the model selection criteria. Simulated data and Monte Carlo experiments show that unless the binary data is extremely unbalanced the semi-parametric and parametric models perform equally well. However, if the data is extremely unbalanced the maximum likelihood estimation does not converge whereas the Bayesian algorithms do. An application is also presented.

Sprache
Englisch

Erschienen in
Series: Working Paper ; No. 2013-08

Klassifikation
Wirtschaft
Semiparametric and Nonparametric Methods: General
Multiple or Simultaneous Equation Models: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
Bayesian Analysis: General
Thema
Semi-parametric binary response models
Markov Chain Monte Carlo algorithms
Kernel densities
Optimal bandwidth
Receiver operating characteristic curve

Ereignis
Geistige Schöpfung
(wer)
Shen, Xiangjin
Li, Shiliang
Tsurumi, Hiroki
Ereignis
Veröffentlichung
(wer)
Rutgers University, Department of Economics
(wo)
New Brunswick, NJ
(wann)
2013

Handle
Letzte Aktualisierung
20.09.2024, 08:22 MESZ

Datenpartner

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Objekttyp

  • Arbeitspapier

Beteiligte

  • Shen, Xiangjin
  • Li, Shiliang
  • Tsurumi, Hiroki
  • Rutgers University, Department of Economics

Entstanden

  • 2013

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