Rank estimators for monotonic index models

We present a new class of rank estimators of scaled coefficients in semiparametric monotonic linear index models. The estimators require no subjective bandwidth choices and have attractive computational properties. We establish root n-consistency and asymptotic normality, and provide the general form and consistent estimators of the asymptotic covariance matrix. We also provide a generalization covering single equation multiple-indices models satisfying certain monotonicity constraints. An analogue of consistency when all explanatory variables are categorical is established, and an application is presented. (C) 1998 Elsevier Science S.A. All rights reserved.