Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Stochastic Frontier Model× | Data Envelopment Analysis (Productivity)× | |
|---|---|---|
| Область | Экономика | Экономика |
| Семейство≠ | Regression model | Process / pipeline |
| Год появления≠ | 1977 | 1978 |
| Автор метода≠ | Aigner, Lovell & Schmidt; Meeusen & van den Broeck | Charnes, Cooper & Rhodes (building on Farrell 1957) |
| Тип≠ | Parametric stochastic production/cost frontier with composed error | Nonparametric linear-programming efficiency frontier |
| Основополагающий источник≠ | 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 ↗ | 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 ↗ |
| Другие названия | SFM, Stochastic Production Frontier, Composed-Error Frontier Model, Parametric Frontier Estimation | DEA Efficiency Analysis, Nonparametric Frontier Efficiency, CCR/BCC Efficiency Measurement, Production Frontier DEA |
| Связанные≠ | 3 | 5 |
| Сводка≠ | 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. | 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. |
| ScholarGateНабор данных ↗ |
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