Statistical Inference for Aggregation of Malmquist Productivity Indices

Abstract

A Comprehensive Set of Asymptotic Properties for a Meaningful Aggregation of Malmquist Indices The Malmquist productivity index (MPI) has become one of the most widely used tools for analyzing dynamic performance of decision-making units. Whereas accounting for economic weights of individual units in aggregations of indices is emphasized in the literature, statistical theory for constructing confidence intervals and performing hypothesis tests based on weighted aggregation of the MPI are still unavailable. In “Statistical Inference for Aggregation of Malmquist Productivity Indices,” Pham, Simar, and Zelenyuk use a novel approach (based on the uniform delta method) to develop new asymptotic theory (including new central limit theorems) for aggregate MPIs as the basis for the statistical inference and test. They also verify the finite-sample performance of their approach via extensive Monte Carlo experiments and provide an illustration using real-world data.

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