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

DOI10.1016/j.ejor.2019.02.040zbMath1430.62067OpenAlexW2915684465WikidataQ128321278 ScholiaQ128321278MaRDI QIDQ1737521

Paul W. Wilson, Léopold Simar

Publication date: 23 April 2019

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://dial.uclouvain.be/pr/boreal/fr/object/boreal%3A201909/datastream/PDF_01/view

zbMATH Keywords

DEAhypothesis testasymptoticinferenceMalmquist index

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Applications of statistics in engineering and industry; control charts (62P30)

Related Items (3)

Investigating the performance of Chinese banks over 2007--2014 ⋮ Corporate Social Responsibility and Firms’ Dynamic Productivity Change ⋮ Technical, allocative and overall efficiency: estimation and inference

Uses Software

• DEA

Cites Work

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• Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis
• ASYMPTOTICS AND CONSISTENT BOOTSTRAPS FOR DEA ESTIMATORS IN NONPARAMETRIC FRONTIER MODELS
• Estimating and bootstrapping Malmquist indices
• Fundamentals of production theory
• Dimension reduction in nonparametric models of production
• Statistical inference for DEA estimators of directional distances
• The hyperbolic-oriented efficiency measure as a remedy to infeasibility of super efficiency models
• Central limit theorems for conditional efficiency measures and tests of the ‘separability’ condition in non‐parametric, two‐stage models of production
• WHEN BIAS KILLS THE VARIANCE: CENTRAL LIMIT THEOREMS FOR DEA AND FDH EFFICIENCY SCORES

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