Computational Statistics

, Volume 33, Issue 3, pp 1429–1455 | Cite as

Semiparametric estimation of the link function in binary-choice single-index models

  • Alan P. KerEmail author
  • Abdoul G. Sam
Original Paper


We propose a new, easy to implement, semiparametric estimator for binary-choice single-index models which uses parametric information in the form of a known link (probability) function and nonparametrically corrects it. Asymptotic properties are derived and the finite sample performance of the proposed estimator is compared to those of the parametric probit and semiparametric single-index model estimators of Ichimura (J Econ 58:71–120, 1993) and Klein and Spady (Econometrica 61:387–421, 1993). Results indicate that if the parametric start is correct, the proposed estimator achieves significant bias reduction and efficiency gains compared to Ichimura (1993) and Klein and Spady (1993). Interestingly, the proposed estimator still achieves significant bias reduction and efficiency gains even if the parametric start is not correct.


Bias reduction Link function Parametric start 


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Institute for the Advanced Study of Food and Agricultural PolicyDepartment of Food, Agricultural and Resource Economics, University of GuelphGuelphCanada
  2. 2.Department of Agricultural, Environmental and Development EconomicsThe Ohio State UniversityColumbusUSA

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