השוואת שיטות

	ניתוח גבול סטוכסטי (SFA)	רגרסיית ריבועים פחותים רגילים (OLS)
תחום	אקונומטריקה	אקונומטריקה
משפחה	Regression model	Regression model
שנת המקור	1977	2019
הוגה השיטה	Aigner, Lovell & Schmidt (1977); Battese & Coelli (1995) for panels	Wooldridge (textbook treatment); classical least squares
סוג	Frontier regression model	Linear regression
מקור מכונן	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
כינויים	SFA, stochastic frontier model, stochastic production frontier, Stokastik Sınır Analizi (SFA)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
קשורות	3	5
תקציר	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).