@TechReport{dp-505,
  author        = {Schwiebert, Jörg},
  astring       = {Jörg Schwiebert},
  title         = {Semiparametric Estimation of a Binary Choice Model with
                  Sample Selection},
  month         = {October},
  year          = {2012},
  pages         = {35},
  size          = {311},
  number        = {505},
  language      = {en},
  keywords      = {Sample selection model, binary dependent variable,
                  semiparametric estimation, control function approach,
                  endogenous covariates},
  jelclass      = {C21, C24, C25, C26},
  abstract      = {In this paper we provide semiparametric estimation
                  strategies for a sample selection model with a binary
                  dependent variable. To the best of our knowledge, this has
                  not been done before. We propose a control function
                  approach based on two di erent identifying assumptions.
                  This gives rise to semiparametric estimators which are
                  extensions of the Klein and Spady (1993), maximum score
                  (Manski, 1975) and smoothed maximum score (Horowitz, 1992)
                  estimators. We provide Monte Carlo evidence and an
                  empirical example to study the nite sample properties of
                  our estimators. Finally, we outline an extension of these
                  estimators to the case of endogenous covariates.}
}