The Quantile Regression Approach to Efficiency Measurement: Insights from Monte Carlo Simulations

ChunpingLiu, Audrey Laporte and Brian Ferguson

Health, Econometrics and Data Group (HEDG) Working Papers from HEDG, c/o Department of Economics, University of York

Abstract: 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)
Date: 2007-07
New Economics Papers: this item is included in nep-ecm, nep-eff and nep-hea
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
https://www.york.ac.uk/media/economics/documents/herc/wp/07_14.pdf Main text (application/pdf)

Related works:
Journal Article: The quantile regression approach to efficiency measurement: insights from Monte Carlo simulations (2008)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:yor:hectdg:07/14

Access Statistics for this paper

More papers in Health, Econometrics and Data Group (HEDG) Working Papers from HEDG, c/o Department of Economics, University of York HEDG/HERC, Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom. Contact information at EDIRC.
Bibliographic data for series maintained by Jane Rawlings ().