เปรียบเทียบวิธี

	การวิเคราะห์ข้อมูลแบบซองจดหมายแบบหน้าต่าง	ดัชนีผลิตภาพมัลม์ควิสต์	Network DEA
สาขาวิชา	การวิเคราะห์ประสิทธิภาพ	การวิเคราะห์ประสิทธิภาพ	การวิเคราะห์ประสิทธิภาพ
ตระกูล	Regression model	Regression model	Regression model
ปีกำเนิด	1984	1994	2000
ผู้ริเริ่ม	Charnes, Clark, Cooper & Golany	Färe, Grosskopf, Norris & Zhang	Färe & Grosskopf
ประเภท	Non-parametric panel efficiency model	Non-parametric productivity index	Multi-stage nonparametric efficiency model
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	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
ที่เกี่ยวข้อง	2	1	2
สรุป	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.