Details

Autor(en) / Beteiligte

• Bădin, Luiza
• Daraio, Cinzia
• Simar, Léopold

Titel

A bootstrap approach for bandwidth selection in estimating conditional efficiency measures

Ist Teil von

• European journal of operational research, 2019-09, Vol.277 (2), p.784-797

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2019

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •Investigation of practical aspects of bandwidth selection for conditional efficiency.•Impact of the bandwidth choice on the estimation of conditional efficiency measures.•We propose a bootstrap approach for selecting the bandwidth.•Various Monte Carlo scenarios confirm the performances of our bootstrap approach.•Practical implementation details and Matlab codes are provided. 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.