Big and beautiful? On non-parametrically measuring scale economies in non-convex technologies.

Knowledge on the scale economies drives the incentives of regulators, governments and individual utilities to scale-up or scale-down the scale of operations. This paper considers the returns to scale (RTS) in non-convex frontier models. In particular, we evaluate RTS assumptions in a Free Disposal Hull model, which accounts for uncertainty and heterogeneity in the sample. Additionally, we provide a three-step framework to empirically analyze the existence and extent of RTS in real world applications. In a .rst step, the presence of scale (and scope) economies is veri.ed. Secondly, RTS for individual observations are examined while in a third step we derive the optimal scale for a sector as a whole. The framework is applied to the Portuguese drinking water sector where we .nd the optimal scale to be situated around 7 to 10 million m3.

A General Framework to Evaluate Economic Efficiency with an Application to British SME

This article formalises the idea of money-metric production frontiers, which we propose as a general framework for nonparametric evaluation of economic efficiency. As we show in our methodological discussion, this improves the flexibility and economic interpretation of our model. The empirical part is the first attempt to test the existence of a size-efficiency relationship among small businesses in the United Kingdom. It is based on a unique panel both with respect to size — ranging from agriculture to services — and to the ten year time span. We employ statistically robust methods to estimate and analyse sectoral efficiency. Our analysis yields three main insights: (1) Average sectors are expected to be two to four times less efficient than those on the efficient frontier. Great dispersion of efficiency scores highlights the importance of dynamic out-of-equilibrium modelling. (2) There is no evidence of a general economy-wide size-efficiency relationship. (3) Economic efficiency remained constant over the past ten years.Small and medium enterprises; economic efficiency; firm size; robust efficiency estimation

A gamma-moment approach to monotonic boundaries estimation

The estimation of optimal support boundaries under the monotonicity constraint is relatively unexplored and still in full development. This article examines a new extreme-value based model which provides a valid alternative for completely envelopment frontier models that often super from lack of precision, and for purely stochastic ones that are known to be sensitive to model misspecification. We provide different motivating applications including the estimation of the minimal cost in production activity and the assessment of the reliability of nuclear reactors

Stochastic non-smooth envelopment of data : semi-parametric frontier estimation subject to shape constraints

The field of productive efficiency analysis is currently divided between two main paradigms: the deterministic, nonparametric Data Envelopment Analysis (DEA) and the parametric Stochastic Frontier Analysis (SFA). This paper examines an encompassing semiparametric frontier model that combines the DEA-type nonparametric frontier, which satisfies monotonicity and concavity, with the SFA-style stochastic homoskedastic composite error term. To estimate this model, a new two-stage method is proposed, referred to as Stochastic Non-smooth Envelopment of Data (StoNED). The first stage of the StoNED method applies convex nonparametric least squares (CNLS) to estimate the shape of the frontier without any assumptions about its functional form or smoothness. In the second stage, the conditional expectations of inefficiency are estimated based on the CNLS residuals, using the method of moments or pseudolikelihood techniques. Although in a cross-sectional setting distinguishing inefficiency from noise in general requires distributional assumptions, we also show how these can be relaxed in our approach if panel data are available. Performance of the StoNED method is examined using Monte Carlo simulations.v2012o

Secure data storage and retrieval in cloud computing

Nowadays cloud computing has been widely recognised as one of the most inuential information technologies because of its unprecedented advantages. In spite of its widely recognised social and economic benefits, in cloud computing customers lose the direct control of their data and completely rely on the cloud to manage their data and computation, which raises significant security and privacy concerns and is one of the major barriers to the adoption of public cloud by many organisations and individuals. Therefore, it is desirable to apply practical security approaches to address the security risks for the wide adoption of cloud computing

Nonparametric Econometric Methods and Application

The present Special Issue collects a number of new contributions both at the theoretical level and in terms of applications in the areas of nonparametric and semiparametric econometric methods. In particular, this collection of papers that cover areas such as developments in local smoothing techniques, splines, series estimators, and wavelets will add to the existing rich literature on these subjects and enhance our ability to use data to test economic hypotheses in a variety of fields, such as financial economics, microeconomics, macroeconomics, labor economics, and economic growth, to name a few