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Seminonparametric Estimation of Binary-Choice Models with an Application to Labor-Force Participation

Listed author(s):
  • Gabler, Siegfried
  • Laisney, Francois
  • Lechner, Michael

The authors adapt the estimation method proposed by A. R. Gallant and D. N. Nychka (1987) to binary-choice models. They present Monte Carlo and asymptotic comparisons with the probit estimator and discuss optimization algorithms, choice of starting values, and strategies f or choosing the number of parameters used in approximating the density. Seminonparametric estimation is almost as efficient as probit estimation in normal samples and performs better in nonnormal sample s. The estimation of a participation model with 3,658 observations and 21 explanatory variables demonstrates the practicability of this approa ch on personal computers.

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Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 11 (1993)
Issue (Month): 1 (January)
Pages: 61-80

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Handle: RePEc:bes:jnlbes:v:11:y:1993:i:1:p:61-80
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