Estimating scale economies in non-convex production models

The literature on nonparametric frontier technologies lacks a method for the measurement of scale economies in non-convex settings. This paper proposes a general procedure which is based on the minimization of the ray average cost and requires the solution of a single programming problem. Our approach allows for multiple optima to introduce the case of global sub-constant scale economies, and it also permits the estimation of scale economies at a local level. The empirical application investigates the role of replicability and the relationship between global and local indicators. It also points out the managerial implications for companies operating in the Italian public transit industry.     

Negative data in DEA: a simple proportional distance function approach

The need to adapt Data Envelopment Analysis (DEA) and other frontier models in the context of negative data has been a rather neglected issue in the literature. A recent article in this journal proposed a variation on the directional distance function, a very general distance function that is dual to the profit function, to accommodate the occurrence of negative data. In this contribution, we define and recommend a generalised Farrell proportional distance function that can do the same job and that maintains a proportional interpretation under mild conditions.     

Hedonic price function estimation in economics and marketing: revisiting Lancaster’s issue of “noncombinable” goods

Following Lancaster’s (J. Political Econ. 74(1):132–157, 1966; Variety, equity and efficiency, 1979) interpretation of his characteristics approach to consumer theory, this contribution focuses on theoretical and empirical arguments questioning the smoothness of traditional hedonic price estimation techniques. Lancaster argued strongly against “combinability”, i.e., that any efficient combination of characteristics is feasible and sensible. We therefore explicitly test the impact of convexity using a set of recent non-parametric estimators. The test is carried out on a sample of 114 digital cameras whose price evolution is followed over 6 months. The hypothesis of convexity is rejected using the Li (Econ. Rev. 15(3):261–274, 1996) test. The conclusions point out implications for economics and marketing.     

Comparing efficiency across markets: An extension and critique of the methodology

The use of non-parametric frontier methods for the evaluation of product market efficiency in heterogeneous markets seems to have gained some popularity recently. However, the statistical properties of these frontier estimators have been largely ignored. The main point is that non-parametric frontier estimators are biased and that the degree of bias depends on specific sample properties, most importantly sample size and number of dimensions of the model. To investigate the effect of this bias on comparing market efficiency, this contribution estimates the efficiency for several datasets for two main product categories. Following (Zhang, Y., Bartels, R., 1998. The effect of sample size on the mean efficiency in DEA with an application to electricity distribution in Australia, Sweden and New Zealand. Journal of Productivity Analysis, 9(3), 187-204.), these results comprise re-estimates for the larger samples limiting their size to that of the smaller samples when the model dimensions for different samples are identical. Furthermore, sample sizes are adjusted to mitigate the eventual differences in dimensions in specification. This allows comparing market efficiency for different markets on a more equal footing, since it reduces the bias effect to a minimum making the comparison of market efficiency possible. However, the article also points out the critical limitations of this [Zhang, Y., Bartels, R., (1998). The effect of sample size on the mean efficiency in DEA with an application to electricity distribution in Australia, Sweden and New Zealand. Journal of Productivity Analysis 9 (3), 187–204] approach in certain respects. Apart from reporting these negative results, we also offer some suggestions for future work.     

A note on a variant of radial measure capable of dealing with negative inputs and outputs in DEA

The recent contribution by Cheng et al. (2013) presents a variant of the traditional radial input- and output-oriented efficiency measures whereby original values are replaced with absolute values. This comment spells out that this article contains some imprecisions and therefore presents some further results.     

Ensemble-averaging and correlation techniques for flow visualization images

Flow visualization using marking techniques such as timelines provides a basis for quantitative analysis of macroscale features of unsteady flows by global ensemble-averaging and correlation techniques. In the visual-ensemble-averaging technique described herein, the timeline positions are tracked and averaged in successive images. The phase reference for the averaging process can take the form of an analog pressure, velocity, or displacement signal, or a recurring coherent portion of the image. Global correlations of the timeline patterns are obtained using the same timelines defined for the ensemble-averaging process. A new type of visual correlation function, giving the correlation between two timelines in a given image or successive images, is proposed. Preliminary results are given.     

Vibration of complex structures: The modal constraint method

A method is described for establishing the natural frequencies of an arbitrary structure with arbitrary supports. The method is based on the modal constraint technique described in a previous paper [1]. As shown in the present paper Weinstein's theory for the intermediate problem can be regarded as equivalent to the Lagrangian multiplier method: i.e., both methods result in the same eigenvalue equations. Weinstein's theory deals with modifications of base differential operators whereas the Lagrangian multiplier method deals with modifications of base energy functionals. The modal constraint technique is an extension of Weinstein's theory, or in energy terms the generalized Fourier expansion of the Lagrangian multiplier. The merits of this method lie in the fact that the eigenvalues and eigenfunctions of structures are used as base structures. The coupling of these structures are taken into account by Lagrangian generalized forces of the constraint acting on the base structures. Some examples are given and the results compared with known solutions.     

Geometric representation of the mean–variance–skewness portfolio frontier based upon the shortage function

The literature suggests that investors prefer portfolios based on mean, variance and skewness rather than portfolios based on mean–variance (MV) criteria solely. Furthermore, a small variety of methods have been proposed to determine mean–variance–skewness (MVS) optimal portfolios. Recently, the shortage function has been introduced as a measure of efficiency, allowing to characterize MVS optimal portfolios using non-parametric mathematical programming tools. While tracing the MV portfolio frontier has become trivial, the geometric representation of the MVS frontier is an open challenge. A hitherto unnoticed advantage of the shortage function is that it allows to geometrically represent the MVS portfolio frontier. The purpose of this contribution is to systematically develop geometric representations of the MVS portfolio frontier using the shortage function and related approaches.     

Benchmarking mean-variance portfolios using a shortage function: the choice of direction vector affects rankings!

In addition to its use in data envelopment analysis models, the shortage function has been proposed as a tool to gauge performance in multi-moment portfolio models. An open issue is how the choice of direction vector affects the efficiency measurement, especially when some of the data can be negative and, from a practical point of view, whether and how the resulting league tables are affected. This paper illustrates empirically how the choice of direction vector affects the relative ranking of mean-variance portfolios. This result is relevant to all frontier-based applications, especially those where some of the data can be naturally negative.