Local NLLS estimation of semi-parametric binary choice models

Published

Journal Article

Summary In this paper, non-linear least squares (NLLS) estimators are proposed for semi-parametric 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 and the smoothed maximum score (SMS) estimator. Two simple bias correction methods-a proposed jackknife method and an alternative non-linear 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. © 2013 Royal Economic Society.

Full Text

Cited Authors

  • Blevins, JR; Khan, S

Published Date

  • June 1, 2013

Published In

Volume / Issue

  • 16 / 2

Start / End Page

  • 135 - 160

Electronic International Standard Serial Number (EISSN)

  • 1368-423X

International Standard Serial Number (ISSN)

  • 1368-4221

Digital Object Identifier (DOI)

  • 10.1111/j.1368-423X.2012.00393.x

Citation Source

  • Scopus