The StoNED age: the departure into a new era of efficiency analysis? A monte carlo comparison of StoNED and the “oldies” (SFA and DEA)
Based on the seminal paper of Farrell (J R Stat Soc Ser A (General) 120(3):253–290, 1957), researchers have developed several methods for measuring efficiency. Nowadays, the most prominent representatives are nonparametric data envelopment analysis (DEA) and parametric stochastic frontier analysis (SFA), both introduced in the late 1970s. Researchers have been attempting to develop a method which combines the virtues—both nonparametric and stochastic—of these “oldies”. The recently introduced Stochastic non-smooth envelopment of data (StoNED) by Kuosmanen and Kortelainen (J Prod Anal 38(1):11–28, 2012) is such a promising method. This paper compares the StoNED method with the two “oldies” DEA and SFA and extends the initial Monte Carlo simulation of Kuosmanen and Kortelainen (J Prod Anal 38(1):11–28, 2012) in several directions. We show, among others, that, in scenarios without noise, the rivalry is still between the “oldies”, while in noisy scenarios, the nonparametric StoNED PL now constitutes a promising alternative to the SFA ML.
KeywordsEfficiency Stochastic non-smooth envelopment of data (StoNED) Data envelopment analysis (DEA) Stochastic frontier analysis (SFA) Monte carlo simulation
JEL ClassificationC14 C52 D24 L59
We are deeply indebted to the participants of the 8th Asia-Pacific Productivity Conference (APPC) in Bangkok, Thailand, the 4th Workshop on Efficiency and Productivity Analysis (HAWEPA) in Halle, Germany, the 12th European Workshop on Efficiency and Productivity Analysis (EWEPA) in Verona, Italy, and the 11th IAEE European Conference in Vilnius, Lithuania, for providing valuable comments that have led to a considerable improvement of earlier versions of this paper. Furthermore, we would like to thank Brian Bloch, Finn Førsund, William Greene, Arne und Geraldine Henningsen, Uwe Jensen, Choonjoo Lee, Colin Vance, the editors and two anonymous referees for their helpful comments and suggestions. The authors are responsible for all errors and omissions.
- Andor M, Hesse F (2011) A Monte Carlo simulation comparing DEA, SFA and two simple approaches to combine efficiency estimates. CAWM Discussion Papers 51, Center of Applied Economic Research Münster (CAWM), University of MünsterGoogle Scholar
- Caudill SB, Ford JM, Gropper DM (1995) Frontier estimation and firm-specific inefficiency measures in the presence of heteroscedasticity. J Bus Econ Stat 13:105–111Google Scholar
- Coelli TJ, Rao DSP, O’ Donnell CJ, Battese GE (2005) An introduction to efficiency and productivity analysis. Springer, BerlinGoogle Scholar
- Fan Y, Li Q, Weersink A (1996) Semiparametric estimation of stochastic production frontier models. J Bus Econ Stat 14(4):460–468Google Scholar
- Hadri K (1999) Estimation of a doubly heteroscedastic stochastic frontier cost function. J Bus Econ Stat 17(3):359–363Google Scholar
- Kumbhakar SC, Lovell CAK (2003) Stochastic frontier analysis. Cambridge University Press, CambridgeGoogle Scholar
- Kuosmanen T (2008) Representation theorem for convex nonparametric least squares. Econ J 11(2):308–325Google Scholar
- Kuosmanen T (2012a) Stochastic semi-nonparametric frontier estimation of electricity distribution networks: application of the StoNED method in the Finnish regulatory model. Energy Economics p doi: 10.1016/j.eneco.2012.03.005
- Kuosmanen T (2012b) Web site: StoNED Stochastic Nonparametric Envelopment of Data: http://www.nomepre.net/index.php/computations
- Mortimer D (2002) Competing Methods for Effciency Measurement: A systematic review of direct DEA vs SFA/DFA comparisons., Centre for Health Program Evaluation (CHPE), Working Paper 136Google Scholar
- Winsten CB (1957) Discussion on Mr. Farrell’s paper. J R Stat Soc Ser A (General) 120(3):282–284Google Scholar