Inference for aggregate efficiency: theory and guidelines for practitioners
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Publication:6586270
DOI10.1016/J.EJOR.2024.01.028MaRDI QIDQ6586270
Shirong Zhao, Léopold Simar, Valentin Zelenyuk
Publication date: 13 August 2024
Published in: European Journal of Operational Research (Search for Journal in Brave)
efficiencydata envelopment analysisfree disposal hullaggregate efficiencynon-parametric efficiency estimators
Cites Work
- Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis
- Measuring the efficiency of decision making units
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- A computationally efficient, consistent bootstrap for inference with non-parametric DEA estimators
- Asymptotic distribution of conical-hull estimators of directional edges
- Aggregating Farrell efficiencies with private and public inputs
- Central limit theorems and inference for sources of productivity change measured by nonparametric Malmquist indices
- Dimension reduction in nonparametric models of production
- On aggregate Farrell efficiencies
- Data sharpening for improving central limit theorem approximations for data envelopment analysis-type efficiency estimators
- Improving finite sample approximation by central limit theorems for estimates from data envelopment analysis
- Technical, allocative and overall efficiency: estimation and inference
- The FDH estimator for productivity efficiency scores
- On Testing Equality of Distributions of Technical Efficiency Scores
- Measurement of Productivity and Efficiency
- Central Limit Theorems for Aggregate Efficiency
- Central limit theorems for conditional efficiency measures and tests of the ‘separability’ condition in non‐parametric, two‐stage models of production
- WHEN BIAS KILLS THE VARIANCE: CENTRAL LIMIT THEOREMS FOR DEA AND FDH EFFICIENCY SCORES
- Statistical Approaches for Non‐parametric Frontier Models: A Guided Tour
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