Distribution-free and link-free estimation for a multivariate semiparametric sample selection model

Marie Chavent (1, 2) , Jérôme Saracco (2, 3)

1 IMB - Institut de Mathématiques de Bordeaux
2 CQFD - Quality control and dynamic reliability
3 GREThA - Groupe de Recherche en Economie Théorique et Appliquée

Most of the prevalent estimation methods for sample selection model rely heavely on parametric assumptions. We consider in this communication a multivariate semiparametric sample selection model and we develop a geometric approach to the estimation of the slope vectors in the outcome equation and in the selection equation. Contrary to most existing methods, we deal symmetrically with both slope vectors. The estimation method is link-free and distribution-free, it works in two main steps: a multivariate Sliced Inverse Regression step, and a Canonical Analysis step. We establish √n-consistency and asymptotic normality of the estimates. We give results from a simulation study in order to illustrate the estimation method.

Mots clés: Multivariate Sliced Inverse Regression; Canonical Analysis; Semipara- metric Regression Models

Domaines: Théorie [stat.TH]; Statistiques [math.ST]

Marie Chavent, Jérôme Saracco. Distribution-free and link-free estimation for a multivariate semiparametric sample selection model. ASMDA 2007, May 2007, Chania, Crète, Greece. ⟨hal-00278290⟩