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| Data Envelopment Analysis (Productivity)× | Stochastic Frontier Analysis (SFA)× | |
|---|---|---|
| Dziedzina≠ | Ekonomia | Ekonometria |
| Rodzina≠ | Process / pipeline | Regression model |
| Rok powstania≠ | 1978 | 1977 |
| Twórca≠ | Charnes, Cooper & Rhodes (building on Farrell 1957) | Aigner, Lovell & Schmidt (1977); Battese & Coelli (1995) for panels |
| Typ≠ | Nonparametric linear-programming efficiency frontier | Frontier regression model |
| Źródło pierwotne≠ | 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 ↗ | 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 ↗ |
| Inne nazwy | DEA Efficiency Analysis, Nonparametric Frontier Efficiency, CCR/BCC Efficiency Measurement, Production Frontier DEA | SFA, stochastic frontier model, stochastic production frontier, Stokastik Sınır Analizi (SFA) |
| Pokrewne≠ | 5 | 3 |
| Podsumowanie≠ | 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. | 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. |
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