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| Malmquist生产率指数× | 窗口数据包络分析× | |
|---|---|---|
| 领域 | 效率分析 | 效率分析 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1994 | 1984 |
| 提出者≠ | Färe, Grosskopf, Norris & Zhang | Charnes, Clark, Cooper & Golany |
| 类型≠ | Non-parametric productivity index | Non-parametric panel 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 ↗ | Charnes, A., Clark, C. T., Cooper, W. W., & Golany, B. (1984). A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the U.S. Air Forces. Annals of Operations Research, 2(1), 95–112. DOI ↗ |
| 别名 | MPI, Malmquist Index, Malmquist DEA Productivity Index, Malmquist Verimlilik Endeksi | Sliding-Window DEA, Temporal DEA, Rolling-Period DEA, Pencere VZA |
| 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). | Window Data Envelopment Analysis (Window DEA) is a non-parametric panel efficiency method that evaluates decision-making units (DMUs) over time by embedding each DMU's observations across a rolling temporal window into a single cross-sectional DEA problem. Introduced by Charnes, Clark, Cooper, and Golany in 1984, it enables longitudinal efficiency tracking without requiring a full panel, increasing discriminatory power by pooling observations across consecutive periods. |
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