So sánh phương pháp
Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.
| Phân tích bao dữ liệu mạng (Network DEA)× | Chỉ số Năng suất Malmquist× | |
|---|---|---|
| Lĩnh vực | Phân tích hiệu quả | Phân tích hiệu quả |
| Họ | Regression model | Regression model |
| Năm ra đời≠ | 2000 | 1994 |
| Người khởi xướng≠ | Färe & Grosskopf | Färe, Grosskopf, Norris & Zhang |
| Loại≠ | Multi-stage nonparametric efficiency model | Non-parametric productivity index |
| Công trình gốc≠ | Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49. DOI ↗ | 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 ↗ |
| Tên gọi khác | Network Data Envelopment Analysis, Network Efficiency Analysis, Multi-Stage DEA, Ağ Veri Zarflama Analizi | MPI, Malmquist Index, Malmquist DEA Productivity Index, Malmquist Verimlilik Endeksi |
| Liên quan≠ | 2 | 1 |
| Tóm tắt≠ | 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. | 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). |
| ScholarGateBộ dữ liệu ↗ |
|
|