השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| ניתוח מעטפת נתונים רשתי (Network DEA)× | מדד מלמקוויסט לפרודוקטיביות× | |
|---|---|---|
| תחום | ניתוח יעילות | ניתוח יעילות |
| משפחה | Regression model | Regression model |
| שנת המקור≠ | 2000 | 1994 |
| הוגה השיטה≠ | Färe & Grosskopf | Färe, Grosskopf, Norris & Zhang |
| סוג≠ | Multi-stage nonparametric efficiency model | Non-parametric productivity index |
| מקור מכונן≠ | 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 ↗ |
| כינויים | Network Data Envelopment Analysis, Network Efficiency Analysis, Multi-Stage DEA, Ağ Veri Zarflama Analizi | MPI, Malmquist Index, Malmquist DEA Productivity Index, Malmquist Verimlilik Endeksi |
| קשורות≠ | 2 | 1 |
| תקציר≠ | 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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