Making noisy data sing
开发了处理缺失数据和测量误差的生产技术最大似然估计量，允许对劳动内生性和缺失数据模式做不同假设，并应用于智利工业普查数据，与忽略数据缺陷的朴素估计量进行比较。
Maximum-likelihood estimators of production technologies are developed that deal with missing data and measurement errors, making alternative assumptions regarding the endogeneity of labor and missing data patterns. The estimators yield indices of the returns to scale, mean square deviation from the efficient frontier, and (when labor is treated as endogenous) mean square deviation from efficient factor mixes. To gauge the performance of the alternative estimators, they are applied to Chilean industrial census data, and compared with ‘naive’ estimators that do not recognize data imperfections.