How to Bootstrap DEA Estimators: A Monte Carlo Comparison

Mickael Löthgren ()
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Mickael Löthgren: Dept. of Economic Statistics, Stockholm School of Economics, Postal: Stockholm School of Economics, P.O. Box 6501, S-113 83 Stockholm, Sweden

No 223, SSE/EFI Working Paper Series in Economics and Finance from Stockholm School of Economics

Abstract: This 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.

Keywords: Bootstrap; Confidence Intervals; Data Envelopment Analysis; Monte Carlo Simulation (search for similar items in EconPapers)
JEL-codes: C14 C15 D24 (search for similar items in EconPapers)
Pages: 22 pages
Date: 1998-02-10
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Citations: View citations in EconPapers (4)

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