Non-parametric, unconditional quantile estimation for efficiency analysis with an application to Federal Reserve check processing operations

By David C. Wheelock, Paul Wilson. 2005

94 citations recorded by the source corpus.

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, a-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 avoiding the curse of dimensionality that plagues data envelopment analysis (DEA) estimators. Our methods could be used by policymakers to compare inefficiency levels across offices or by managers of individual offices to identify peer offices.

View Paper: https://www.semanticscholar.org/paper/8aa8e96576c19eaa2b284e066f4391337462101c

Citations in the corpus

• Estimation and Inference in Nonparametric Frontier Models: Recent Developments and Perspectives (2013, methodology): “This method is implemented in the FEAR software package described by Wilson (2008). Wilson (1993) provides further details and empirical examples. However, results for the second example in Section 2 of Wilson (1993), based on data from Charnes et al.”
• Inference by the m out of n bootstrap in nonparametric frontier models (2011, background): “Statistical properties of FDH estimators have been examined by Korostelev et al. (1995a), Park et al. (2000), Wheelock and Wilson (2008) , and Wilson (2010).”