Local NLLS Estimation of Semiparametric Binary Choice Models

Jason R. Blevins and Shakeeb Khan.
Working Paper.

Availability:

Abstract. In this paper, nonlinear least squares (NLLS) estimators are proposed for semiparametric binary response models under conditional median restrictions. The estimators can be identical to NLLS procedures for parametric binary response models (e.g. Probit), and consequently have the advantage of being easily implementable using standard software packages such as Stata. This is in contrast to existing estimators for the model, such as the maximum score estimator (Manski, 1975, 1985) and the smoothed maximum score (SMS) estimator (Horowitz, 1992). Simple bias correction methods, such as a proposed “jackknife” method, result in the same rate of convergence as SMS. The results from a Monte Carlo study show that the new estimators perform well in finite samples.

Keywords: binary response, median restriction, NLLS, bias reduction, jackknife.

JEL Classification: C13, C14, C25.

BibTeX Record:

@TechReport{blevins10local,
  author       = {Jason R. Blevins and Shakeeb Khan},
  title        = {Local NLLS Estimation of Semiparametric Binary
                  Choice Models},
  institution  = {Ohio State University},
  year         = 2010,
  type         = {Working Paper}
}