A Generalized Hyperbolic Distance Function for Benchmarking Performance: Estimation and Inference

IF 1.9 4区 经济学 Q2 ECONOMICS
Paul W. Wilson
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引用次数: 0

Abstract

This paper describes a new multiplicative, generalized hyperbolic distance function (GHDF) that allows the researcher to measure technical efficiency while holding a subset of inputs or outputs fixed. This is useful when dealing with “bad” or undesirable outputs, or in applications where some inputs or outputs are regarded as quasi-fixed. The paper provides computational methods for both free-disposal hull and data envelopment analysis estimators of the GHDF. In addition, statistical properties of the estimators are derived, enabling researchers to make inference and test hypotheses. An empirical illustration using data on U.S. credit unions is provided, as well as Monte Carlo evidence on the performance of the estimators. As illustrated in the empirical example, estimates of the GHDF are easier to interpret than estimates of additive, directional distance functions that until know have been the only non-parametric estimator of efficiency allowing subsets of input our outputs to be held constant.

Abstract Image

用于性能基准测试的广义双曲距离函数:估计与推理
本文介绍了一种新的乘法广义双曲距离函数(GHDF),它允许研究人员在固定投入或产出子集的情况下衡量技术效率。这在处理 "坏的 "或不理想的产出时,或在某些投入或产出被视为准固定的应用中非常有用。本文为 GHDF 的自由处置船体和数据包络分析估计器提供了计算方法。此外,还推导了估计器的统计属性,使研究人员能够进行推理和假设检验。本文利用美国信贷联盟的数据进行了实证说明,并提供了有关估计器性能的蒙特卡罗证据。正如实证示例所示,GHDF 的估计值比加法方向性距离函数的估计值更容易解释,而加法方向性距离函数是迄今为止唯一一种允许输入和输出子集保持不变的非参数效率估计值。
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来源期刊
Computational Economics
Computational Economics MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.00
自引率
15.00%
发文量
119
审稿时长
12 months
期刊介绍: Computational Economics, the official journal of the Society for Computational Economics, presents new research in a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems from all branches in economics. The topics of Computational Economics include computational methods in econometrics like filtering, bayesian and non-parametric approaches, markov processes and monte carlo simulation; agent based methods, machine learning, evolutionary algorithms, (neural) network modeling; computational aspects of dynamic systems, optimization, optimal control, games, equilibrium modeling; hardware and software developments, modeling languages, interfaces, symbolic processing, distributed and parallel processing
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