Single period Markowitz portfolio selection, performance gauging and duality: a variation on Luenberger's shortage function

Markowitz portfolio theory (1952) has induced research into the efficiency of portfolio management. This paper studies existing nonparametric efficiency measurement approaches for single period portfolio selection from a theoretical perspective and generalises currently used efficiency measures into the full mean-variance space. Therefore, we introduce the efficiency improvement possibility function (a variation on the shortage function), study its axiomatic properties in the context of Markowitz efficient frontier, and establish a link to the indirect mean-variance utility function. This framework allows distinguishing between portfolio efficiency and allocative efficiency. Furthermore, it permits retrieving information about the revealed risk aversion of investors. The efficiency improvement possibility function thus provides a more general framework for gauging the efficiency of portfolio management using nonparametric frontier envelopment methods based on quadratic optimisation.

Absolute and relative deprivation and the measurement of poverty

This paper develops the link between poverty and inequality by focussing on a class of poverty indices (some of them well-known) which aggregate normative concerns for absolute and relative deprivation. The indices are distinguished by a parameter that captures the ethical sensitivity of poverty measurement to ``exclusion'' or ``relative-deprivation'' aversion. We also show how the indices can be readily used to predict the impact of growth on poverty. An illustration using LIS data finds that he United States show more relative deprivation than Denmark and Belgium whatever the percentiles considered, but that overall deprivation comparisons of the four countries considered will generally necessarily depend on the intensity of the ethical concern for relative deprivation. The impact of growth on poverty is also seen to depend on the presence of and on the attention granted to concerns over relative deprivation. }

Regional income inequalities in Europe: an updated measurement and some decomposition results

In this paper well-known summary inequality indexes are used to explore interregional income inequalities in Europe. In particular, we mainly employ Theils’population-weighted index because of its appealing properties. Two decomposition analysis are applied. First, regional inequalities are decomposed by regional subgroups (countries). Second, intertemporal inequality changes are separated into income and population changes. The main results can be summarized as follows. First, data confirm a reduction in crossregional inequality during 1982-97. Second, this reduction is basically due to real convergence among countries. Third, currently the greater part of European interregional disparities is within-country by nature, which introduce an important challenge for the European policy. Fourth, inequality changes are due mainly to income variations, population changes playing a minor role.

The Total fiscal cost of indirect taxation: an approximation using Catalonia's recent input-output table

In this note we quantify to what extent indirect taxation influences and distorts prices. To do so we use the networked accounting structure of the most recent input-output table of Catalonia, an autonomous region of Spain, to model price formation. The role of indirect taxation is considered both from a classical value perspective and a more neoclassical flavoured one. We show that they would yield equivalent results under some basic premises. The neoclassical perspective, however, offers a bit more flexibility to distinguish among different tax figures and hence provide a clearer disaggregate picture of how an indirect tax ends up affecting, and by how much, the cost structure.

The Measurement of social polarization in a multi-group context

Polarization indices presented up to now have only focused their attention on the distribution of income/wealth. However, in many circumstances income is not the only relevant dimension that might be the cause of social conflict, so it is very important to have a social polarization index able to cope with alternative dimensions. In this paper we present an axiomatic characterization of one of such indices: it has been obtained as an extension of the (income) polarization measure introduced in Duclos, Esteban and Ray (2004) to a wider domain. It turns out that the axiomatic structure introduced in that paper alone is not appropriate to obtain a fully satisfactory characterization of our measure, so additional axioms are proposed. As a byproduct, we present an alternative axiomatization of the aforementioned income polarization measure.

Marine biotoxins in the Catalan littoral: could biosensors be integrated into monitoring programmes?

Aquest article descriu els sensors enzimàtics i immunosensors electroquímics que s’han desenvolupat als nostres grups per a la detecció de la biotoxina marina àcid okadaic (OA), i discuteix la possibilitat d’integrar-los en programes de seguiment. Els sensors enzimàtics per a OA que es presenten es basen en la inhibició de la proteïna fosfatasa (PP2A) per aquesta toxina i la mesura electroquímica de l’activitat enzimàtica mitjançant l’ús de substrats enzimàtics apropiats, electroquímicament actius després de la seva desfosforació per l’enzim. Els immunosensors electroquímics descrits en aquest article es basen en un enzimoimmunoassaig sobre fase sòlida competitiu indirecte (ciELISA), amb fosfatasa alcalina (ALP) o peroxidasa (HRP) com a marcatges, i un sistema de reciclatge enzimàtic amb diaforasa (DI). Els biosensors presentats aquí s’han aplicat a l’anàlisi de dinoflagel·lats, musclos i ostres. Les validacions preliminars amb assaigs colorimètrics i LC-MS/MS han demostrat la possibilitat d’utilitzar les bioeines desenvolupades per al cribratge preliminar de biotoxines marines en mostres de camp o de cultiu, que ofereixen informació complementària a la cromatografia. En conclusió, tot i que encara cal optimitzar alguns paràmetres experimentals, la integració dels biosensors a programes de seguiment és viable i podria proporcionar avantatges respecte a altres tècniques analítiques pel que fa al temps d’anàlisi, la simplicitat, la selectivitat, la sensibilitat, el fet de poder ser d’un sol ús i l’efectivitat de cost. This article describes the electrochemical enzyme sensors and immunosensors that have been developed by our groups for the detection of marine biotoxin okadaic acid (OA), and discusses the possibility of integrating them into monitoring programmes. The enzyme sensors for OA reported herein are based on the inhibition of immobilised protein phosphatase 2A (PP2A) by this toxin and the electrochemical measurement of the enzyme activity through the use of appropriate enzyme substrates, which are electrochemically active after dephosphorylation by the enzyme. The electrochemical immunosensors described in this article are based on a competitive indirect Enzyme- Linked ImmunoSorbent Assay (ciELISA), using alkaline phosphatase (ALP) or horseradish peroxidase (HRP) as labels, and an enzymatic recycling system with diaphorase (DI). The biosensors presented herein have been applied to the analysis of dinoflagellates, mussels and oysters. Preliminary validations with colorimetric assays and LC-MS/MS have demonstrated the possibility of using the developed biotools for the preliminary screening of marine biotoxins in field or cultured samples, offering complementary information to chromatography. In conclusion, although optimisation of some experimental parameters is still required, the integration of biosensors into monitoring programmes is viable and may provide advantages over other analytical techniques in terms of analysis time, simplicity, selectivity, sensitivity, disposability of electrodes and cost effectiveness.

A Data mining approach to indirect inference

Consider a model with parameter phi, and an auxiliary model with parameter theta. Let phi be a randomly sampled from a given density over the known parameter space. Monte Carlo methods can be used to draw simulated data and compute the corresponding estimate of theta, say theta_tilde. A large set of tuples (phi, theta_tilde) can be generated in this manner. Nonparametric methods may be use to fit the function E(phi|theta_tilde=a), using these tuples. It is proposed to estimate phi using the fitted E(phi|theta_tilde=theta_hat), where theta_hat is the auxiliary estimate, using the real sample data. This is a consistent and asymptotically normally distributed estimator, under certain assumptions. Monte Carlo results for dynamic panel data and vector autoregressions show that this estimator can have very attractive small sample properties. Confidence intervals can be constructed using the quantiles of the phi for which theta_tilde is close to theta_hat. Such confidence intervals are found to have very accurate coverage.

Regional value added in Italy over the long run (1891-2001): linking indirect estimates with official figures, and implications

This paper presents value added estimates for the Italian regions, in benchmark years from 1891 until 1951, which are linked to those from official figures available from 1971 in order to offer a long-term picture. Sources and methodology are documented and discussed, whilst regional activity rates and productivity are also presented and compared. Thus some questions are briefly reconsidered: the origins and extent of the north-south divide, the role of migration and regional policy in shaping the pattern of regional inequality, the importance of social capital, and the positioning of Italy in the international debate on regional convergence, where it stands out for the long run persistence of its disparities.

Indirect likelihood inference

Given a sample from a fully specified parametric model, let Zn be a given finite-dimensional statistic - for example, an initial estimator or a set of sample moments. We propose to (re-)estimate the parameters of the model by maximizing the likelihood of Zn. We call this the maximum indirect likelihood (MIL) estimator. We also propose a computationally tractable Bayesian version of the estimator which we refer to as a Bayesian Indirect Likelihood (BIL) estimator. In most cases, the density of the statistic will be of unknown form, and we develop simulated versions of the MIL and BIL estimators. We show that the indirect likelihood estimators are consistent and asymptotically normally distributed, with the same asymptotic variance as that of the corresponding efficient two-step GMM estimator based on the same statistic. However, our likelihood-based estimators, by taking into account the full finite-sample distribution of the statistic, are higher order efficient relative to GMM-type estimators. Furthermore, in many cases they enjoy a bias reduction property similar to that of the indirect inference estimator. Monte Carlo results for a number of applications including dynamic and nonlinear panel data models, a structural auction model and two DSGE models show that the proposed estimators indeed have attractive finite sample properties.

Straightening out the concept of direct and indirect input requirements

Gim & Kim (1998) proposed a generalization of Jeong (1982, 1984) reinterpretation of the Hawkins-Simon condition for macroeconomic stability to off-diagonal matrix elements. This generalization is conceptually relevant for it offers a complementary view of interindustry linkages beyond final or net output influence. The extension is completely similar to the 'total flow' idea introduced by Szyrmer (1992) or the 'output-to-output' multiplier of Miller & Blair (2009). However the practical implementation of Gim & Kim is actually faulty since it confuses the appropriate order of output normalization. We provide a new and elementary solution for the correct formalization using standard interindustry accounting concepts.

Simultaneous estimation of indirect and interaction effects using structural equation models

Interaction effects are usually modeled by means of moderated regression analysis. Structural equation models with non-linear constraints make it possible to estimate interaction effects while correcting formeasurement error. From the various specifications, Jöreskog and Yang's(1996, 1998), likely the most parsimonious, has been chosen and further simplified. Up to now, only direct effects have been specified, thus wasting much of the capability of the structural equation approach. This paper presents and discusses an extension of Jöreskog and Yang's specification that can handle direct, indirect and interaction effects simultaneously. The model is illustrated by a study of the effects of an interactive style of use of budgets on both company innovation and performance

Correcting Measurement Error Bias in Interaction Models with Small Samples

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression

Moderating effects of management control systems and innovation on performance. Simple methods for correcting the effects of measurement error for interaction effects in small samples

In the accounting literature, interaction or moderating effects are usually assessed by means of OLS regression and summated rating scales are constructed to reduce measurement error bias. Structural equation models and two-stage least squares regression could be used to completely eliminate this bias, but large samples are needed. Partial Least Squares are appropriate for small samples but do not correct measurement error bias. In this article, disattenuated regression is discussed as a small sample alternative and is illustrated on data of Bisbe and Otley (in press) that examine the interaction effect of innovation and style of use of budgets on performance. Sizeable differences emerge between OLS and disattenuated regression

Intraocular Straylight And Contrast Sensitivity Two Months After Lasik: Intraocular Straylight Measurement As A New Parameter In Visual Quality Assessment

PROPÒSIT: Estudiar l'efecte de la cirurgia LASIK en la llum dispersa i la sensibilitat al contrast. MÈTODES: Vint-i-vuit pacients van ser tractats amb LASIK. La qualitat visual es va avaluar abans de l'operació i dos mesos després. RESULTATS: La mitjana de llum dispersa i la sensibilitat al contrast abans de l'operació no va canviar en dos mesos després. Només un ull tenia un marcat augment en la llum dispersa. Nou ulls van presentar una lleugera disminució en la sensibilitat al contrast. S'han trobat dues complicacions. CONCLUSIÓ: Després de LASIK la majoria dels pacients (80%) no van tenir complicacions i van mantenir la seva qualitat visual. Uns pocs pacients (16%) van tenir una mica de qualitat visual disminuïda. Molt pocs (4%) van tenir complicacions clíniques amb disminució en la qualitat visual.