Sammenlign metoder

	Stochastic Frontier Model	Data Envelopment Analysis (Productivity)	Stokastisk frontanalyse (SFA)
Fagområde	Økonomi	Økonomi	Økonometri
Familie	Regression model	Process / pipeline	Regression model
Oprindelsesår	1977	1978	1977
Ophavsperson	Aigner, Lovell & Schmidt; Meeusen & van den Broeck	Charnes, Cooper & Rhodes (building on Farrell 1957)	Aigner, Lovell & Schmidt (1977); Battese & Coelli (1995) for panels
Type	Parametric stochastic production/cost frontier with composed error	Nonparametric linear-programming efficiency frontier	Frontier regression model
Oprindelig kilde	Aigner, D., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21–37.	Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.	Aigner, D., Lovell, C.A.K. & Schmidt, P. (1977). Formulation and Estimation of Stochastic Frontier Production Function Models. Journal of Econometrics, 6(1), 21–37.
Aliasser	SFM, Stochastic Production Frontier, Composed-Error Frontier Model, Parametric Frontier Estimation	DEA Efficiency Analysis, Nonparametric Frontier Efficiency, CCR/BCC Efficiency Measurement, Production Frontier DEA	SFA, stochastic frontier model, stochastic production frontier, Stokastik Sınır Analizi (SFA)
Relaterede	3	5	3
Resumé	The stochastic frontier model is a parametric method for estimating productive efficiency that separates a producer's shortfall from best practice into two parts: genuine inefficiency and random noise. Introduced independently in 1977 by Aigner, Lovell, and Schmidt and by Meeusen and van den Broeck, it specifies a production (or cost) function with a composed error term — a symmetric disturbance for luck and measurement error plus a one-sided, non-negative term for inefficiency — and estimates it by maximum likelihood, yielding firm-specific efficiency scores that, unlike deterministic methods, are robust to statistical noise.	Data envelopment analysis (DEA) is a nonparametric, linear-programming technique for measuring the relative productive efficiency of comparable units — firms, plants, hospitals, schools, bank branches — that convert multiple inputs into multiple outputs. Introduced by Charnes, Cooper, and Rhodes in 1978 and rooted in Farrell's 1957 work on efficiency measurement, it constructs a best-practice frontier that envelops the observed data and scores each unit by its distance to that frontier, requiring no assumed functional form for the production technology.	Stochastic Frontier Analysis is a frontier regression model, introduced by Aigner, Lovell and Schmidt in 1977, that estimates a production, cost, or profit function while separating each unit's technical inefficiency from ordinary statistical noise. It splits the error term into a symmetric random component and a one-sided inefficiency component, producing firm- or country-level efficiency scores.