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| Chỉ số Năng suất Malmquist× | Phân tích Bao trùm Dữ liệu Cửa sổ× | |
|---|---|---|
| Lĩnh vực | Phân tích hiệu quả | Phân tích hiệu quả |
| Họ | Regression model | Regression model |
| Năm ra đời≠ | 1994 | 1984 |
| Người khởi xướng≠ | Färe, Grosskopf, Norris & Zhang | Charnes, Clark, Cooper & Golany |
| Loại≠ | Non-parametric productivity index | Non-parametric panel efficiency model |
| Công trình gốc≠ | 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 ↗ |
| Tên gọi khác | MPI, Malmquist Index, Malmquist DEA Productivity Index, Malmquist Verimlilik Endeksi | Sliding-Window DEA, Temporal DEA, Rolling-Period DEA, Pencere VZA |
| Liên quan≠ | 1 | 2 |
| Tóm tắt≠ | 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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