Performance estimation when the distribution of inefficiency is unknown
提出一种基于快速傅里叶变换的方法，在无效率分布未知时估计随机前沿模型和数据包络分析的绩效得分，蒙特卡洛实验表现良好，应用于美国大银行得到合理结果。
We show how to compute inefficiency or performance scores when the distribution of the one-sided error component in Stochastic Frontier Models (SFMs) is unknown; and we do the same with Data Envelopment Analysis (DEA). Our procedure, which is based on the Fast Fourier Transform (FFT), utilizes the empirical characteristic function of the residuals in SFMs or efficiency scores in DEA. The new techniques perform well in Monte Carlo experiments and deliver reasonable results in an empirical application to large U.S. banks. In both cases, deconvolution of DEA scores with the FFT brings the results much closer to the inefficiency estimates from SFM.