The Limiting Distribution of the Maximum Rank Correlation Estimator
证明Han的最大秩相关估计量具有√n一致性和渐近正态性，给出协方差矩阵的一致估计量，并应用于二元选择模型，发现该估计量未达到半参数效率界。
Han’s maximum rank correlation (MRC) estimator is shown to be√ n-consistent and asymptotically normal. The proof rests on a general method for determining the asymptotic distribution of a maximization estimator, a simple U-statistic decomposition, and a uniform bound for degenerate U-processes. A consistent estimator of the asymptotic covari-ance matrix is provided, along with a result giving the explicit form of this matrix for any model within the scope of the MRC estimator. The latter result is applied to the binary choice model, and it is found that the MRC estimator does not achieve the semiparametric efficiency bound.