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| 네트워크 자료포괄분석 (Network DEA)× | 말퀴스트 생산성 지수(Malmquist Productivity Index)× | |
|---|---|---|
| 분야 | 효율성 분석 | 효율성 분석 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2000 | 1994 |
| 창시자≠ | Färe & Grosskopf | Färe, Grosskopf, Norris & Zhang |
| 유형≠ | Multi-stage nonparametric efficiency model | Non-parametric productivity index |
| 원전≠ | 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 ↗ |
| 별칭 | Network Data Envelopment Analysis, Network Efficiency Analysis, Multi-Stage DEA, Ağ Veri Zarflama Analizi | MPI, Malmquist Index, Malmquist DEA Productivity Index, Malmquist Verimlilik Endeksi |
| 관련≠ | 2 | 1 |
| 요약≠ | 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). |
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