Stochastic Frontier Model
The stochastic frontier model is a parametric method for estimating productive efficiency that separates a producer's shortfall from best practice into two parts: genuine inefficiency and random noise. Introduced independently in 1977 by Aigner, Lovell, and Schmidt and by Meeusen and van den Broeck, it specifies a production (or cost) function with a composed error term — a symmetric disturbance for luck and measurement error plus a one-sided, non-negative term for inefficiency — and estimates it by maximum likelihood, yielding firm-specific efficiency scores that, unlike deterministic methods, are robust to statistical noise.
手法の全文を読む
無料アカウントでログインすると、このセクションを読めます。
手法マップ
関連する手法の近傍 — ノードを選択して探索できます。
出典
- Aigner, D., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21–37. DOI: 10.1016/0304-4076(77)90052-5 ↗
- Meeusen, W., & van den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 18(2), 435–444. DOI: 10.2307/2525757 ↗
このページの引用方法
ScholarGate. (2026, June 22). Stochastic Frontier Production Function Model. ScholarGate. https://scholargate.app/ja/economics/stochastic-frontier-analysis
どの手法を選ぶ？
この手法を最も近い類縁の手法と並べ、両者を見比べてください — ライブラリは本を机の上に並べるだけ。選ぶのはあなたです。
- Data Envelopment Analysis (Productivity)経済学↔ 比較
- 効率に基づくランキングのためのデータ包絡分析（CCRモデル）意思決定↔ 比較
- 確率的フロンティア分析 (SFA)計量経済学↔ 比較