The Quantile Regression Approach to Efficiency Measurement: Insights from Monte Carlo Simulations
Audrey Laporte and
Health, Econometrics and Data Group (HEDG) Working Papers from HEDG, c/o Department of Economics, University of York
In the health economics literature there is an ongoing debate over approaches used to estimate the efficiency of health systems at various levels, from the level of the individual hospital- or nursing home –up to that of the health system as a whole. The two most widely used approaches to evaluating the efficiency with which various units deliver care are non-parametric Data Envelopment Analysis (DEA) and parametric Stochastic Frontier Analysis (SFA). Productivity researchers tend to have very strong preferences over which methodology to use for efficiency estimation. In this paper, we use generated experimental datasets and Monte Carlo simulation to compare the performance of DEA and SFA in terms of their ability to accurately estimate efficiency. We also evaluate Quantile regression as a potential alternative approach. A Cobb-Douglas production function, random error terms and a technical inefficiency term with different distributions are used to calculate the observed output. The results, based on these experiments, suggest that neither DEA nor SFA can be regarded as clearly dominant, and that Quantile regression because it yields more reliable estimates, represents a useful alternative approach in efficiency studies.
Keywords: Technical efficiency; data envelopment analysis; stochastic frontier estimation; quantile regression. (search for similar items in EconPapers)
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Journal Article: The quantile regression approach to efficiency measurement: insights from Monte Carlo simulations (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:yor:hectdg:07/14
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