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| Stochastische Grenzweranalyse (SFA)× | Methode der kleinsten Quadrate (OLS)× | |
|---|---|---|
| Fachgebiet | Ökonometrie | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1977 | 2019 |
| Urheber≠ | Aigner, Lovell & Schmidt (1977); Battese & Coelli (1995) for panels | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Frontier regression model | Linear regression |
| Wegweisende Quelle≠ | 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. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Aliasnamen | SFA, stochastic frontier model, stochastic production frontier, Stokastik Sınır Analizi (SFA) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Verwandt≠ | 3 | 5 |
| Zusammenfassung≠ | 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). |
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