A bootstrap approach for bandwidth selection in estimating conditional efficiency measures
| Autor: | Cinzia Daraio, Luiza Badin, Léopold Simar |
|---|---|
| Přispěvatelé: | UCL - SSH/LIDAM/ISBA - Institut de Statistique, Biostatistique et Sciences Actuarielles |
| Rok vydání: | 2019 |
| Předmět: |
Information Systems and Management
General Computer Science Computer science Monte Carlo method 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research Industrial and Manufacturing Engineering Cross-validation Bandwidth Modelling and Simulation 0502 economics and business Statistics Monte Carlo 050210 logistics & transportation 021103 operations research Nonparametric estimator 05 social sciences Bandwidth (signal processing) Conditional efficiency Conditional probability distribution Bootstrap Data Envelopment Analysis (DEA)/Free Disposal Hull (FDH) Modeling and Simulation Smoothing |
| Zdroj: | European Journal of Operational Research, Vol. 277, p. 784-797 (2019) |
| ISSN: | 0377-2217 |
| DOI: | 10.1016/j.ejor.2019.02.054 |
| Popis: | Conditional efficiency measures are needed when the production process does not depend only on the inputs and outputs, but may be influenced by external factors and/or environmental variables (Z). They are estimated by means of a nonparametric estimator of the conditional distribution function of the inputs and outputs, conditionally on values of Z. For doing this, smoothing procedures and smoothing parameters, the bandwidths, are involved. So far, Least Squares Cross Validation (LSCV) methods have been used, which have been proven to provide bandwidths with optimal rates for estimating conditional distributions. In efficiency analysis, the main interest is in the estimation of the conditional efficiency score, which typically depends on the boundary of the support of the distribution and not on the full conditional distribution. In this paper, we show indeed that the rate for the bandwidths which is optimal for estimating conditional distributions, may not be optimal for the estimation of the efficiency scores. We propose hence a new approach based on the bootstrap which overcomes these difficulties. We analyze and compare, through Monte Carlo simulations, the performances of LSCV techniques with our bootstrap approach in finite samples. As expected, our bootstrap approach shows generally better performances and is more robust to the various Monte Carlo scenarios analyzed. We also illustrate our methodology through an empirical example using an US Aggressive-Growth Mutual Funds data set. |
| Databáze: | OpenAIRE |
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