Estimating production technologies using multi-output adaptive constrained enveloping splines
Data Envelopment Analysis (DEA) is a widely used method for evaluating the relative efficiency of decision-making units, but it often yields overly optimistic efficiency estimates, particularly with small sample sizes. To overcome this limitation, we introduce Adaptive Constrained Enveloping Splines...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad Miguel Hernández de Elche |
| Repositorio: | REDIUMH. Depósito Digital de la UMH |
| OAI Identifier: | oai:dspace.umh.es:11000/38615 |
| Acceso en línea: | https://hdl.handle.net/11000/38615 |
| Access Level: | acceso abierto |
| Palabra clave: | data envelopment analysis multi-output technologies overfitting machine learning CDU::3 - Ciencias sociales::31 - Demografía. Sociología. Estadística CDU::5 - Ciencias puras y naturales::51 - Matemáticas::517 - Análisis |
| Sumario: | Data Envelopment Analysis (DEA) is a widely used method for evaluating the relative efficiency of decision-making units, but it often yields overly optimistic efficiency estimates, particularly with small sample sizes. To overcome this limitation, we introduce Adaptive Constrained Enveloping Splines (ACES), a non-parametric technique based on regression splines to accommodate multi-output, multi-input production contexts. ACES employs a three-stage estimation process. In the first stage, optimal output levels are estimated while incorporating essential envelope constraints, with optional monotonicity and/or concavity adjustments as needed. In the second stage, a refinement phase is carried out in which some of the estimates made are replaced by the observed values. Finally, a DEA-type technology is constructed using a new virtual data sample, ensuring adherence to usual shape constraints. Although ACES entails a higher computational cost, it achieves substantially lower mean squared error and bias than alternative methods of the literature across a wide range of simulated scenarios. This improvement is particularly pronounced in settings with complex production structures or heterogeneous returns to scale. This performance is consistent across both noise-free and noisy data environments, underscoring the method’s robustness and accuracy. |
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