Root-n consistency of intercept estimators in a binary response model under tail restrictions - TAN, Lili (Institutional Knowledge at Singapore Management University, 2018)
The intercept of the binary response model is irregularly identiﬁed when the supports of both the special regressor V and the error term ε are the whole real line. This leads to the estimator of the intercept having potentially a slower than √n convergence rate, which can result in a large estimation error in practice. This paper imposes addition tail restrictions which guarantee the regular identiﬁcation of the intercept and thus the √n-consistency of its estimator. We then propose an estimator that achieves the √n rate. Finally, we extend our tail restrictions to a full-blown model with endogenous regressors.