Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/62132 
Erscheinungsjahr: 
2012
Schriftenreihe/Nr.: 
CAWM Discussion Paper No. 60
Verlag: 
Westfälische Wilhelms-Universität Münster, Centrum für Angewandte Wirtschaftsforschung (CAWM), Münster
Zusammenfassung: 
Based on the seminal paper of Farrell (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. Since decades, 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 (2010) is 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 (2010) in two directions. Firstly, we consider a wider range of conditions. Secondly, we also consider the maximum likelihood estimator (ML) and the pseudolikelihood estimator (PL) for SFA and StoNED, respectively. We show 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.
Schlagwörter: 
efficiency
stochastic non-smooth envelopment of data (StoNED)
data envelopment analysis (DEA)
stochastic frontier analysis (SFA)
monte carlo simulation
JEL: 
C1
C5
D2
L5
Q4
Dokumentart: 
Working Paper

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