Making noisy data sing

Abstract

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.

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