Local NLLS Estimation of Semiparametric Binary Choice Models

Alternative nonlinear regression functions

Jason R. Blevins and Shakeeb Khan.
Forthcoming at Econometrics Journal.

Availability:

Published version
Working paper (September 2012)

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). Two simple bias correction methods—a proposed jackknife method and an alternative nonlinear regression function—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, nonlinear least squares, bias reduction, jackknife.

JEL Classification: C13, C14, C25.

BibTeX Record:

@Article{blevins-khan-2013,
  author       = {Jason R. Blevins and Shakeeb Khan},
  title        = {Local NLLS Estimation of Semiparametric Binary
                  Choice Models},
  journal      = {Econometrics Journal},
  year         = {forthcoming},
  doi          = {10.1111/j.1368-423X.2012.00393.x}
}