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美国商业银行效率与生产率变化的稳健非参数分位数估计：1985—2004年AI 解读

This article uses a new nonparametric, unconditional, hyperbolic order-αquantile estimator to construct a hyperbolic version of the Malmquist index. Unlike traditional nonparametric efficiency estimators, the new estimator is both robust to data outliers and has a root-n convergence rate. We use this estimator to examine changes in the efficiency and productivity of U.S. banks between 1985 and 2004. We find that larger banks experienced larger efficiency and productivity…

效率分析的非参数无条件分位数估计：美联储支票处理业务应用AI 解读

This paper examines the technical efficiency of U.S. Federal Reserve check processing offices over 1980–2003. We extend results from Park et al. (2000) and Daouia and Simar (2007) to develop an unconditional, hyperbolic, α-quantile estimator of efficiency. Our new estimator is fully non-parametric and robust with respect to outliers; when used to estimate distance to quantiles lying close to the full frontier, it is strongly consistent and converges at rate root-n, thus…