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| Malmquist生产率指数× | 网络数据包络分析 (Network DEA)× | |
|---|---|---|
| 领域 | 效率分析 | 效率分析 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1994 | 2000 |
| 提出者≠ | Färe, Grosskopf, Norris & Zhang | Färe & Grosskopf |
| 类型≠ | Non-parametric productivity index | Multi-stage nonparametric efficiency model |
| 开创性文献≠ | Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84(1), 66–83. link ↗ | Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49. DOI ↗ |
| 别名 | MPI, Malmquist Index, Malmquist DEA Productivity Index, Malmquist Verimlilik Endeksi | Network Data Envelopment Analysis, Network Efficiency Analysis, Multi-Stage DEA, Ağ Veri Zarflama Analizi |
| 相关≠ | 1 | 2 |
| 摘要≠ | The Malmquist Productivity Index (MPI) is a non-parametric measure of total factor productivity (TFP) change over time. Formally grounded in distance functions by Caves, Christensen, and Diewert (1982) and operationalized using Data Envelopment Analysis by Färe, Grosskopf, Norris, and Zhang (1994), MPI decomposes productivity growth into two components: efficiency change (catching-up to the frontier) and technical change (shift of the frontier itself). | Network Data Envelopment Analysis (Network DEA) is a nonparametric efficiency measurement framework introduced by Färe and Grosskopf (2000) that extends classical DEA to multi-stage or multi-division production processes. Rather than treating a decision-making unit as a black box, it explicitly models the internal structure — the divisions and the intermediate products that flow between them — enabling stage-level and overall efficiency scores to be estimated simultaneously within a single coherent model. |
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