When analyzing what determines the efficiency of production, regressing efficiency scores estimated by DEA on explanatory variables has much intuitive appeal. Simar and Wilson (2007) show that this naïve two-stage estimation procedure suffers from severe flaws, that render its results, and in...

This study is intended to assess the introduction of increased capitalization requirements for Ukrainian insurance firms. To do so, we employ up-to-date frontier efficiency analysis The analysis suggests that an increase in size occurs not only because of the regulator?s requirements, but also...

In this paper we compare two flexible estimators of technical efficiency in a cross-sectional setting: the nonparametric kernel SFA estimator of Fan, Li and Weersink (1996) to the nonparametric bias corrected DEA estimator of Kneip, Simar and Wilson (2008). We assess the finite sample...

When analyzing what determines the efficiency of production, regressing efficiency scores estimated by DEA on explanatory variables has much intuitive appeal. Simar and Wilson (2007) show that this nai͏̈ve two-stage estimation procedure suffers from severe flaws, that render its results, and...

In this paper we compare two flexible estimators of technical efficiency in a cross-sectional setting: the nonparametric kernel SFA estimator of Fan, Li and Weersink (1996) to the nonparametric bias corrected DEA estimator of Kneip, Simar and Wilson (2008). We assess the finite sample...

This study is intended to assess the introduction of increased capitalization requirements for Ukrainian insurance firms. To do so, we employ up-to-date frontier efficiency analysis. The analysis suggests that an increase in size occurs not only because of the regulator's requirements, but also...

We employ bootstrap techniques in a production frontier framework to provide statistical inference for each component in the decomposition of labor productivity growth, which has essentially been ignored in this literature. We show that only two of the four components have significantly...