Browsing by Subject "semiparametric estimation"

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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.
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    Semiparametric Two Stage Estimation of Sample Selection Models Subject to Tobit-Type Selection Rules
    (Center for Economic Research, Department of Economics, University of Minnesota, 1990-07) Lee, Lung-Fei
    A semiparametric two stage estimation method is proposed for the estimation of sample selection models which are subject to Tobit-type selection rules. With randomization restrictions on the disturbances of the model, all the regression coefficients in the model are in general identifiable without exclusion restrictions. The proposed estimator is shown to be Vn-consistent and asymptotically normal. Some Monte Carlo results to demonstrate its finite sample performance are also provided.