Estimating production technologies using multi-output adaptive constrained enveloping splines
提出自适应约束包络样条（ACES）方法，通过三阶段估计过程解决数据包络分析在小样本下效率估计过于乐观的问题，在复杂生产结构下均方误差和偏差更低。
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.