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Double Bootstrap Confidence Intervals in the Two-Stage DEA Approach

Chronopoulos, DK and Girardone, C and Nankervis, JC (2015) 'Double Bootstrap Confidence Intervals in the Two-Stage DEA Approach.' Journal of Time Series Analysis, 36 (5). 653 - 662. ISSN 0143-9782

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Contextual factors usually assume an important role in determining firms' productive efficiencies. Nevertheless, identifying them in a regression framework might be complicated. The problem arises from the efficiencies being correlated with each other when estimated by Data Envelopment Analysis, rendering standard inference methods invalid. Simar and Wilson (2007) suggest the use of bootstrap algorithms that allow for valid statistical inference in this context. This article extends their work by proposing a double bootstrap algorithm for obtaining confidence intervals with improved coverage probabilities. Moreover, acknowledging the computational burden associated with iterated bootstrap procedures, we provide an algorithm based on deterministic stopping rules, which is less computationally demanding. Monte Carlo evidence shows considerable improvement in the coverage probabilities after iterating the bootstrap procedure. The results also suggest that percentile confidence intervals perform better than their basic counterpart.

Item Type: Article
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HB Economic Theory
Divisions: Faculty of Social Sciences > Essex Business School
Faculty of Social Sciences > Essex Business School > Essex Finance Centre
Depositing User: Jim Jamieson
Date Deposited: 20 Aug 2015 14:05
Last Modified: 30 Mar 2021 23:15

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