【主讲】Jin Yan Assistant Professor of Economics (The Chinese University of Hong Kong)
【主题】A Smoothed Maximum Score (SMS) Estimator for Multinomial Discrete Choice Models
【时间】2014年10月17日 (周五) 15：30-17：00
【地点】上海财经大学经济学院楼801室
【语言】英文
【摘要】We propose a semiparametric estimator that allows for heteroskedasticity for multinomial discrete choice models. The proposed estimator is obtained by maximizing a smoothed version of Manski's score function using a pairwise scoring rule initially proposed by Manski (1975) and later developed by Fox (2007). We prove the strong consistency and asymptotic normality of the estimator. The rate of convergence of the SMS estimator for multinomial choice models is not affected by the number of alternatives and can be made arbitrarily close to the inverse of square root N, which is the same as the rate of convergence of Horowitz’s (1992) SMS estimator for the binary choice model. Monte Carlo experiments provide evidence that the proposed estimator has a smaller mean squared error than both the multinomial logit (MNL) estimator and the maximum score (MS) estimator when heteroskedasticity exists. We illustrate the method with an empirical application estimating college decisions using Chilean data
