ScholarGate
アシスタント

手法を比較

選択した手法を並べて確認できます。異なる行はハイライト表示されます。

マルムクィスト生産性指数×ウィンドウデータ包絡分析×
分野効率性分析効率性分析
系統Regression modelRegression model
提唱年19941984
提唱者Färe, Grosskopf, Norris & ZhangCharnes, Clark, Cooper & Golany
種類Non-parametric productivity indexNon-parametric panel efficiency model
原典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 ↗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. DOI ↗
別名MPI, Malmquist Index, Malmquist DEA Productivity Index, Malmquist Verimlilik EndeksiSliding-Window DEA, Temporal DEA, Rolling-Period DEA, Pencere VZA
関連12
概要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).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.
ScholarGateデータセット
  1. v1
  2. 2 出典
  3. PUBLISHED
  1. v1
  2. 1 出典
  3. PUBLISHED

検索へ スライドをダウンロード

ScholarGate手法を比較: Malmquist Productivity Index · Window DEA. 2026-06-18に以下より取得 https://scholargate.app/ja/compare