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en
Article de revue
Este ítem está publicado en
Statistica Sinica. 2010, vol. 20, n° 2, p. 513-536
Taipei : Institute of Statistical Science, Academia Sinica
Resumen en inglés
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 ...Leer más >
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 distributionfree. It works in two main steps: a multivariate sliced inverse regression step, and a canonical analysis step. We establish pn-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.< Leer menos
Palabras clave en inglés
Sliced Inverse Regression (SIR)
Multivariate SIR
Canonical Analysis
Semiparametric Regression Models
Eigen-decomposition
Orígen
Importado de HalCentros de investigación