Article ID: | iaor20119402 |
Volume: | 36 |
Issue: | 2 |
Start Page Number: | 219 |
End Page Number: | 230 |
Publication Date: | Oct 2011 |
Journal: | Journal of Productivity Analysis |
Authors: | Kuosmanen Timo, Johnson L |
Keywords: | statistics: data envelopment analysis, statistics: regression, simulation: applications |
Understanding the effects of operational conditions and practices on productive efficiency can provide valuable economic and managerial insights. The conventional approach is to use a two‐stage method where the efficiency estimates are regressed on contextual variables representing the operational conditions. The main problem of the two‐stage approach is that it ignores the correlations between inputs and contextual variables. To address this shortcoming, we build on the recently developed regression interpretation of data envelopment analysis (DEA) to develop a new one‐stage semi‐nonparametric estimator that combines the nonparametric DEA‐style frontier with a regression model of the contextual variables. The new method is referred to as stochastic semi‐nonparametric envelopment of