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Center for Economic Research Discussion Papers

Persistent link for this collectionhttps://hdl.handle.net/11299/54175

This series of discussion papers arose from seminars and research conducted at the Center for Economic Research and includes work by three Nobel Prize in Economics laureates.

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Browsing Center for Economic Research Discussion Papers by Subject "Asymptotic efficiency"

Now showing 1 - 2 of 2
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    On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models
    (Center for Economic Research, Department of Economics, University of Minnesota, 1990-09) Lee, Lung-Fei
    This article has considered methods of simulated moments for estimation of discrete response models. We have introduced a modified method of simulated moments of McFadden [1989]. Using the same number of Monte Carlo draws as in McFadden's method of simulated moments, our estimator is asymptotically efficient relative to McFadden's estimator. In addition to the method of simulated moments, we have considered also maximum simulated likelihood estimation methods. The estimators are shown to be consistent and asymptotically normal without excessive number of Monte Carlo draws.
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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.