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A Study Of A Semiparametric Binary Choice Model With Integrated Covariates


  • Guerre, Emmanuel
  • Moon, Hyungsik Roger


This paper studies a semiparametric nonstationary binary choice model. Imposing a spherical normalization constraint on the parameter for identification purposes, we find that the maximum score estimator and smoothed maximum score estimator are at least [square root of n]-consistent. Comparing this rate to the convergence rate of the parametric maximum likelihood estimator (MLE), we show that when a normalization restriction is imposed on the parameter, the Park and Phillips (2000, Econometrica 68, 1249–1280) parametric MLE converges at a rate of n3/4 and its limiting distribution is a mixed normal. Finally, we show briefly how to apply our estimation method to a nonstationary single-index model.The first draft of the paper was written while Guerre was visiting the economics department of the University of Southern California. We thank Peter C.B. Phillips, a co-editor, and three anonymous referees for helpful comments and John Dolfin for proofreading. Guerre thanks the economics department of the University of Southern California for its hospitality during his visit. Moon appreciates financial support of the University of Southern California faculty development award.

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  • Guerre, Emmanuel & Moon, Hyungsik Roger, 2006. "A Study Of A Semiparametric Binary Choice Model With Integrated Covariates," Econometric Theory, Cambridge University Press, vol. 22(4), pages 721-742, August.
  • Handle: RePEc:cup:etheor:v:22:y:2006:i:04:p:721-742_06

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    Cited by:

    1. Chen, Le-Yu & Lee, Sokbae, 2018. "Best subset binary prediction," Journal of Econometrics, Elsevier, vol. 206(1), pages 39-56.

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