Browsing by Subject "Semiparametric model"

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    Efficient Semiparametric Scoring Estimation of Sample Selection Models
    (Center for Economic Research, Department of Economics, University of Minnesota, 1990-02) Lee, Lung-Fei
    A semi parametric profile likelihood method is proposed for estimation of sample selection models. The method is a two step scoring semi parametric estimation procedure based on index formulation and kernel density estimation. Under some regularity conditions, the estimator is asymptotically normal. This method can be applied to estimation of general sample selection models with multiple regimes and sequential choice models with selectivity. For the binary choice sample selection model, the estimator is asymptotically efficiency in the sense that its asymptotic variance matrix attains the asymptotic bound of G. Chamberlain.
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    Semiparametric Instrumental Variable Estimation of Simultaneous Equation Sample Selection Models
    (Center for Economic Research, Department of Economics, University of Minnesota, 1991-06) Lee, Lung-Fei
    The identification and estimation of a semiparametric simultaneous equation model with selectivity have been considered. The identification of structural parameters from reduced form parameters in the semi parametric model requires stronger conditions than the usual rank condition in the classical simultaneous equation model or the parametric simultaneous equation sample selection model. The necessary order condition for identification in the semiparametric model corresponds to the over-identification condition in the classical model. Semiparametric two-stage estimation methods which generalize the two-stage least squares method and the generalized two-stage least squares method for the parametric model are introduced. The semi parametric generalized least squares estimator is shown to be asymptotically efficient in a class of semiparametric instrumental variable estimators.
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    Semiparametric Maximum Profile Likelihood Estimation of Polytomous and Sequential Choice Models
    (Center for Economic Research, Department of Economics, University of Minnesota, 1989-12) Lee, Lung-Fei
    This article considers semiparametric estimation of discrete choice models. The estimation methods are some semiparametric maximum profile likelihood methods which generalize Klein and Spady [1987] to the estimation of polytomous choice and sequential choice models. Special emphases are on the correction of asymptotic bias and negative density estimates caused by high order kernel density estimation. The estimators are shown to be Vn consistent and asymptotically normal. They attain the asymptotic efficiency bound of semiparametric estimation with some infinite dimensional parameter spaces of index probability functions.