A semiparametric approach for a multivariate sample selection model

Marie Chavent*, Benoît Liquet, Jérôme Saracco

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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 √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.

Original languageEnglish
Pages (from-to)513-536
Number of pages24
JournalStatistica Sinica
Issue number2
Publication statusPublished - Apr 2010
Externally publishedYes


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


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