Σύγκριση μεθόδων

	Ανάλυση Δεδομένων Ενίσχυσης Παραθύρου	Δείκτης Παραγωγικότητας Malmquist
Πεδίο	Ανάλυση Αποδοτικότητας	Ανάλυση Αποδοτικότητας
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	1984	1994
Δημιουργός	Charnes, Clark, Cooper & Golany	Färe, Grosskopf, Norris & Zhang
Τύπος	Non-parametric panel efficiency model	Non-parametric productivity index
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	Sliding-Window DEA, Temporal DEA, Rolling-Period DEA, Pencere VZA	MPI, Malmquist Index, Malmquist DEA Productivity Index, Malmquist Verimlilik Endeksi
Συναφείς	2	1
Σύνοψη	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).