A comparison of parametric and nonparametric estimation methods for cost frontiers and economic measures

Abstract

This article examines the empirical performance of alternative frontier estimators’ ability to replicate a known underlying technology and economic measures such as multi-product and product-specific economies of scale, and economies of scope. A cross sectional Monte Carlo procedure to simulate data is used to evaluate a two-sided error system, an OLS system restricting errors to be above the cost frontier, the stochastic frontier method, and data envelopment analysis (DEA). The data are generated assuming a half-normal distribution, and a uniform distribution. Data were also simulated with single and two output firms. The DEA estimator was most robust in estimating the “true” cost frontier and associated economic measures including data sets without single output firms and less effected by distributional assumptions.

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