Duality theory in empirical work, revisited
通过蒙特卡洛模拟生成伪数据集，校准已知初始技术参数的美国农业数据，检验对偶理论在仅存在技术异质性企业加总噪声时的估计准确性。
We compute a pseudo-dataset by Monte Carlo simulations featuring important characteristics of US agriculture, such that the initial technology parameters are known, and employing widely used datasets for calibration. Then, we show the usefulness of this calibration by applying the duality theory approach to datasets bearing as sources of noise only the aggregation of technologically heterogeneous firms. Estimation recovers initial parameters with reasonable accuracy. These conclusions are expected, but the proposed calibration sets the basis for analysing the performance of duality theory in empirical work when datasets have more observed and unobserved sources of noise, as those faced by practitioners.