This paper develops a consistent bootstrap estimation procedure to obtain confidence intervals for nonparametric measures of productive efficiency. Although the methodology is illustrated in terms of technical efficiency measured by output distance functions, the technique can be easily extented...

Conventional approaches for inference about efficiency in parametric stochastic frontier (PSF) models are based on percentiles of the estimated distribution of the one-sided error term, conditional on the composite error. When used as prediction intervals, coverage is poor when the...

In a single index Poisson regression model with unknown link function, the index parameter can be root- n consistently estimated by the method of pseudo maximum likelihood. In this paper, we study, by simulation arguments, the practical validity of the asymptotic behaviour of the pseudo maximum...

In this paper we extend the work of Simar (2007) introducing noise in nonparametric frontier models. We develop an approach that synthesizes the best features of the two main methods in the estimation of production efficiency. Specifically, our approach first allows for statistical noise,...

When analyzing productivity and efficiency of firms, stochastic frontier models are very attractive because they allow, as in typical regression models, to introduce some noise in the Data Generating Process. Most of the approaches so far have been using very restrictive fully parametric...

In this paper we propose a very flexible estimator in the context of truncated regression that does not require parametric assumptions. To do this, we adapt the theory of local maximum likelihood estimation. We provide the asymptotic results and illustrate the performance of our estimator on...

In this paper we investigate economies of scale and scope of European universities.The proposed approach builds on the notion that university production is a multi-input multi-output process different than standard production activity. The analyses are based on an interesting database which...

Directional distance functions provide very flexible tools for investigating the performance of Decision Making Units (DMUs). Their flexibility relies on their ability to handle undesirable outputs and to account for non-discretionary inputs and/or outputs by fixing zero values in some elements...