# The Limiting Distribution of the Maximum Rank Correlation Estimator

https://doi.org/0012-9682(199301)61:1<123:TLDOTM>2.0.CO;2-J
p. 123-137

Robert P. Sherman

Han's maximum rank correlation (MRC) estimator is shown to be $\sqrt n$-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 covariance 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.