The nonlinear limited-information maximum- likelihood estimator and the modified nonlinear two-stage least-squares estimator |
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Authors: | Takeshi Amemiya |
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Institution: | Stanford University, Stanford, Calif. 94305, U.S.A. |
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Abstract: | Several limited-information type estimators of the nonlinear simultaneous equation model are considered and their asymptotic covariance matrices are compared. Amemiya (1974) proposed the general class of nonlinear two-stage least-squares estimators. In this paper, its two specific members are considered and, in addition, the nonlinear limited-information maximum- likelihood estimator and the modified nonlinear two-stage least-squares estimator are proposed. Both are shown to be asymptotically more efficient than the nonlinear two-stage least-squares estimator, and the second has the advantage of being computationally simple. |
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