পদ্ধতির তুলনা করুন

	উইন্ডো ডেটা এনভেলপমেন্ট অ্যানালাইসিস	মালমকুইস্ট উৎপাদনশীলতা সূচক
ক্ষেত্র	দক্ষতা বিশ্লেষণ	দক্ষতা বিশ্লেষণ
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1984	1994
প্রবর্তক	Charnes, Clark, Cooper & Golany	Färe, Grosskopf, Norris & Zhang
ধরন	Non-parametric panel efficiency model	Non-parametric productivity index
মৌলিক উৎস	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.
অপর নাম	Sliding-Window DEA, Temporal DEA, Rolling-Period DEA, Pencere VZA	MPI, Malmquist Index, Malmquist DEA Productivity Index, Malmquist Verimlilik Endeksi
সম্পর্কিত	2	1
সারসংক্ষেপ	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).