方法对比

	Stochastic Frontier Model	随机前沿分析 (SFA)
领域	经济学	计量经济学
方法族	Regression model	Regression model
起源年份	1977	1977
提出者	Aigner, Lovell & Schmidt; Meeusen & van den Broeck	Aigner, Lovell & Schmidt (1977); Battese & Coelli (1995) for panels
类型	Parametric stochastic production/cost frontier with composed error	Frontier regression model
开创性文献	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.	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.
别名	SFM, Stochastic Production Frontier, Composed-Error Frontier Model, Parametric Frontier Estimation	SFA, stochastic frontier model, stochastic production frontier, Stokastik Sınır Analizi (SFA)
相关	3	3
摘要	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.	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.