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有序响应模型中的平移限制与半参数估计AI 解读

We develop a √n-consistent and asymptotically normal estimator of the parameters (regression coefficients and threshold points) of a semiparametric ordered response model under the assumption of independence of errors and regressors. The independence assumption implies shift restrictions allowing identification of threshold points up to location and scale. The estimator is useful in various applications, particularly in new product demand forecasting from survey data subject…

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二元响应模型的有效半参数估计量AI 解读

This paper proposes an estimator for discrete choice models that makes no assumption concerning the functional form of th e choice probability function where this function can be characterized by an index. The estimator is shown to be consistent, asymptotically normally distributed, and to achieve the semiparametric efficiency bound. Monte Carlo evidence indicates that there may be only modest efficiency losses relative to maximum likelihood estimation when the distribution…

k 类估计量：有限样本中的最优归一化AI 解读

Frequently, in estimating an equation, one is interested in a particular set of normalized coefficients. There is still a normalization problem, however, for if an equation contains m 1 endogenous variables, there are m 1 ways to estimate the same coefficients. The purpose of this article is to determine the optimum normalization for finite samples. Restricting the analysis to k-class estimators and employing Kadane's small sample technique [9], let p 2 i denote the…