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Stochastic Frontier Model×Data Envelopment Analysis (Productivity)×
OblastEkonomijaEkonomija
PorodicaRegression modelProcess / pipeline
Godina nastanka19771978
TvoracAigner, Lovell & Schmidt; Meeusen & van den BroeckCharnes, Cooper & Rhodes (building on Farrell 1957)
TipParametric stochastic production/cost frontier with composed errorNonparametric linear-programming efficiency frontier
Temeljni izvorAigner, D., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21–37. DOI ↗Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. DOI ↗
Drugi naziviSFM, Stochastic Production Frontier, Composed-Error Frontier Model, Parametric Frontier EstimationDEA Efficiency Analysis, Nonparametric Frontier Efficiency, CCR/BCC Efficiency Measurement, Production Frontier DEA
Srodne35
SažetakThe 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.
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ScholarGateUporedite metode: Stochastic Frontier Model · Data Envelopment Analysis (Productivity). Preuzeto 2026-06-24 sa https://scholargate.app/sr/compare