A computationally efficient, consistent bootstrap for inference with non-parametric DEA estimators
(2009) , 41 pages
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- Authors
- Abstract
- We develop a tractable, consistent bootstrap algorithm for inference about Farrell-Debreu efficiency scores estimated by non-parametric data envelopment analysis (DEA) methods. The algorithm allows for very general situations where the distribution of the inefficiencies in the input-output space may be heterogeneous. Computational effiiency and tractability are achieved by avoiding the complex double-smoothing procedure in the algorithm proposed by Kneip et al. (2008). In particular, we avoid technical difficulties in the earlier algorithm associated with smoothed estimates of a density with unknown, nonlinear, multivariate bounded support requiring complicated reflection methods. The new procedure described here is relatively simple and easy to implement: for particular values of a pair of smoothing parameters, the computational complexity is the same as the (inconsistent) naive bootstrap. The resulting computational speed allows the bootstrap to be iterated in order to optimize the smoothing parameters. From a practical viewpoint, only standard packages for computing DEA efficiency estimates, i.e., solving linear problems, are required for implementation. The performance of the method in finite samples is illustrated through some simulated examples.
- Affiliations
Universitat Bonn Institut für Gessellschafts- und Wirtschaftswissenschaften, Statistische Abteilung Clemson University The John E. Walker Department of Economics
Citations
Kneip, A., Simar, L., & Wilson, P. (2009). A computationally efficient, consistent bootstrap for inference with non-parametric DEA estimators (STAT Discussion Papers 0903). https://hdl.handle.net/2078.5/33443