Misclassification-robust semiparametric estimation of single-index binary-choice models
提出一类新的半参数间接推断估计量，用于单指标二元选择模型，该估计量在允许辅助准则误设的情况下仍能一致估计参数，并对响应变量的误分类具有稳健性。
Summary In this paper, a new class of semiparametric estimators for single-index binary-choice models is introduced. The proposed estimators are based on the semiparametric indirect inference that identifies and estimates the parameters of the model via possibly misspecified auxiliary criteria. A large class of considered auxiliary criteria includes the ordinary least squares, nonlinear least squares, and nonlinear least absolute deviations estimators. Besides deriving the consistency and asymptotic normality of the proposed methods, we demonstrate that the proposed indirect inference methodology—at least for selected auxiliary criteria—combines weak distributional assumptions, good estimation precision, and robustness to misclassification of responses. We conduct Monte Carlo experiments and an application study to compare the finite-sample performance of the proposed and existing estimators.