Semiparametric Least Squares Estimation of Monotone Single Index Models and Its Application to the Itelative Least Squares Estimation of Binary Choice Models
نویسنده
چکیده
The Semiparametric Least Squares (SLS) estimation for single index models is studied. Applying the isometric regression by Ayer et al (1955), the method minimizes the mean squared errors with respect to both finite and infinite dimensional parameters. A proof of consistency and an upper bound of convergence rates is offered. As an application example of the SLS estimation, asymptotic normality of the Iterative Least Squares (ILS) estimator proposed by Wang and Zhou (1995) is proven. JEL classification: C14; C25
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