How to Bootstrap DEA Estimators: A Monte Carlo Comparison
AbstractThis paper evaluates the performance of three bootstrap algorithms for the data envelopment analysis (DEA) estimator using a Monte Carlo simulation study. The Löthgren and Tambour (1997) (LT) algorithm; the Simar and Wilson (1997b) (SW) algorithm; and a combination of the LT and SW algorithms (the LSW-algorithm) are considered in the study. The empirical coverage accuracy of bootstrap confidence intervals are simulated under both variable returns to scale (VRS) and constant return-to-scale (CRS) restricted DEA estimators. The results indicate that the LSW-algorithm performs slightly better than the LT-algorithm, which in turn performs better than the SW-algorithm.
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Bibliographic InfoPaper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 223.
Length: 22 pages
Date of creation: 10 Feb 1998
Date of revision:
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Bootstrap; Confidence Intervals; Data Envelopment Analysis; Monte Carlo Simulation;
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