Central limit theorems and inference for sources of productivity change measured by nonparametric Malmquist indices

Leopold Simar and Paul Wilson

European Journal of Operational Research, 2019, vol. 277, issue 2, 756-769

Abstract: Malmquist indices are often used to measure productivity changes in dynamic settings and have been widely applied. The indices are typically estimated using data envelopment analysis (DEA) estimators. Malmquist indices are often decomposed into sub-indices that measure the sources of productivity change (e.g., changes in efficiency, technology or other factors). Recently, Kneip et al. (2018) provide new theoretical results enabling inference about productivity change for individual firms as well as average productivity changed measured in terms of geometric means. This paper extends those results to components of productivity change arising from various decompositions of Malmquist indices. New central limit theorems are developed to allow inference about arithmetic means of logarithms of the sub-indices as well as geometric means of (untransformed) sub-indices. The results are quite general and extend to other sub-indices not explicitly considered in this paper.

Keywords: Asymptotic; DEA; Hypothesis test; Inference; Malmquist index (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221719301882
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Central limit theorems and inference for sources of productivity change measured by nonparametric Malmquist indices (2019)
Working Paper: Central Limit Theorems and Inference for Sources of Productivity Change Measured by Nonparametric Malmquist Indices (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:277:y:2019:i:2:p:756-769

DOI: 10.1016/j.ejor.2019.02.040

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().