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Statistical Inference for Aggregation of Malmquist Productivity Indices

Manh D. Pham, Leopold Simar () and Valentin Zelenyuk
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Manh D. Pham: School of Economics and Centre for Efficiency and Productivity Analysis (CEPA) at The University of Queensland, Australia

No WP082019, CEPA Working Papers Series from University of Queensland, School of Economics

Abstract: The Malmquist Productivity Index (MPI) has gained popularity amongst studies on dynamic change of productivity of decision making units (DMUs). In practice, this index is frequently reported at aggregate levels (e.g., public and private rms) in the form of simple equally-weighted arithmetic or geometric means of individual MPIs. A number of studies have emphasized that it is necessary to account for the relative importance of individual DMUs in the aggregations of indices in general and of MPI in particular. While more suitable aggregations of MPIs have been introduced in the literature, their statistical properties have not been revealed yet, preventing applied researchers from making essential statistical inferences such as con dence intervals and hypothesis testing. In this paper, we will ll this gap by developing a full asymptotic theory for an appealing aggregation of MPIs. On the basis of this, some meaningful statistical inferences are proposed and their nite-sample performances are veri ed via extensive Monte Carlo experiments.

Keywords: aggregation; asymptotics; DEA; hypothesis test; inference; Malmquist index; productivity (search for similar items in EconPapers)
JEL-codes: C14 C44 C51 D24 M11 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-eff
Date: 2019-07
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