Finite Sample Performance of Semiparametric Binary Choice Estimators

Strong assumptions needed to correctly specify parametric binary choice probability models make them particularly vulnerable to misspecification. Semiparametric models provide a less restrictive approach with estimators that exhibit desirable asymptotic properties. This paper discusses the standard parametric binary choice models, Probit and Logit, as well as the semiparametric binary choice estimators proposed in Ichimura (1993) and Klein and Spady (1993). A Monte Carlo study suggests that the semiparametric estimators have desirable finite sample properties and outperform their parametric counterparts when the parametric model is misspecified. The semiparametric estimators show only moderate efficiency loss compared to correctly specified parametric