Distinguishing Errors in Measurement from Errors in Optimization
指出，在双重性框架下的典型计量生产实践忽略了扰动来源，而不同来源需要不同的估计方法。典型方法适用于要素投入测量误差而非优化误差。作者提出识别扰动来源的方法，并利用美国农业数据发现优化误差的证据，从而拒绝典型设定。
Typical econometric production practices under duality ignore the source of disturbances. We show that, depending on the source, a different approach to estimation is required. The typical approach applies under errors in factor input measurement rather than errors in optimization. An approach to the identification of disturbance sources is suggested. We find credible evidence in U.S. agriculture of errors in optimization compared to errors of measurement, and thus reject the typical specification.