Сравнение на методи

	Анализ на обхвата на данните (Window Data Envelopment Analysis)	Индекс на производителността на Малмквист	Мрежов анализ на обвиващата функция (Network DEA)
Област	Анализ на ефективността	Анализ на ефективността	Анализ на ефективността
Семейство	Regression model	Regression model	Regression model
Година на възникване	1984	1994	2000
Създател	Charnes, Clark, Cooper & Golany	Färe, Grosskopf, Norris & Zhang	Färe & Grosskopf
Тип	Non-parametric panel efficiency model	Non-parametric productivity index	Multi-stage nonparametric efficiency model
Основополагащ източник	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.	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.	Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49.
Други названия	Sliding-Window DEA, Temporal DEA, Rolling-Period DEA, Pencere VZA	MPI, Malmquist Index, Malmquist DEA Productivity Index, Malmquist Verimlilik Endeksi	Network Data Envelopment Analysis, Network Efficiency Analysis, Multi-Stage DEA, Ağ Veri Zarflama Analizi
Свързани	2	1	2
Резюме	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.	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).	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.