Comparar métodos

	Stochastic Frontier Model	DEA	Análisis de Frontera Estocástica (SFA)
Campo	Economía	Toma de decisiones	Econometría
Familia	Regression model	MCDM	Regression model
Año de origen	1977	1978	1977
Autor original	Aigner, Lovell & Schmidt; Meeusen & van den Broeck	Charnes, A., Cooper, W. W., Rhodes, E.	Aigner, Lovell & Schmidt (1977); Battese & Coelli (1995) for panels
Tipo	Parametric stochastic production/cost frontier with composed error	Non-parametric efficiency frontier (CCR model)	Frontier regression model
Fuente seminal	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	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.
Alias	SFM, Stochastic Production Frontier, Composed-Error Frontier Model, Parametric Frontier Estimation	—	SFA, stochastic frontier model, stochastic production frontier, Stokastik Sınır Analizi (SFA)
Relacionados	3	0	3
Resumen	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.	DEA (Data Envelopment Analysis (CCR model) for efficiency-based ranking) is a dea multi-criteria decision-making (MCDM) method introduced by Charnes, A., Cooper, W. W., Rhodes, E. in 1978. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	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.