A SEMIPARAMETRIC APPROACH FOR A MULTIVARIATE SAMPLE SELECTION MODEL

作者：Chavent Marie^*; Liquet Benoit; Saracco Jerome

来源：Statistica Sinica, 2010, 20(2): 513-536.

• Canonical analysis
• eigen-decomposition
• multivariate SIR
• semiparametric regression models
• sliced inverse regression (SIR)

Most of the common estimation methods for sample selection models rely heavily on parametric and normality assumptions. We consider in this paper a multivariate semiparametric sample selection model and 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. Moreover, 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 root n-consistency and asymptotic normality of the estimates. We describe how to estimate the observation and selection link functions. The theory is illustrated with a simulation study.

• 出版日期2010-4
• 单位INRIA