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In more and more situations, artificially intelligent algorithms have to model humans’ (social) preferences on whose behalf they increasingly make decisions. They can learn these preferences through the repeated observation of human behavior in social encounters. In such a context, do individuals adjust the selfishness or prosociality of their behavior when it is common knowledge that their actions produce various externalities through the training of an algorithm? In an online experiment, we let participants’ choices in dictator games train an algorithm. Thereby, they create an externality on future decision making of an intelligent system that affects future participants. We show that individuals who are aware of the consequences of their training on the pay- offs of a future generation behave more prosocially, but only when they bear the risk of being harmed themselves by future algorithmic choices. In that case, the externality of artificially intelligence training induces a significantly higher share of egalitarian decisions in the present.

With Big Data, decisions made by machine learning algorithms depend on training data generated by many individuals. In an experiment, we identify the effect of varying individual responsibility for the moral choices of an artificially intelligent algorithm. Across treatments, we manipulated the sources of training data and thus the impact of each individual's decisions on the algorithm. Diffusing such individual pivotality for algorithmic choices increased the share of selfish decisions and weakened revealed prosocial preferences. This does not result from a change in the structure of incentives. Rather, our results show that Big Data offers an excuse for selfish behavior through lower responsibility for one's and others' fate.

It is well known among practitioners that the seasonal adjustment applied to economic time series involves several decisions to be made by the econometrician. As such, it would always be desirable to have an informed opinion on the risks taken by each of those decisions. In this paper, I assess which disaggregation strategy delivers the best results for the case of the Chilean 1986–2009 GDP quarterly dataset (base year: 2003). This is done by performing an aggregate-by-disaggregate analysis under different schemes, as the fixed base year dataset allows this fair comparison. The analysis is based on seasonal adjustment diagnostics contained in the X-12-ARIMA program plus some statistical tests for robustness. This exercise is relevant for conjunctural economic assessment, as it concerns signal extraction from seasonal, noisy series, direction of change detection, and econometric applications based on reliable and accurate unobserved variables. The results show that it is preferable, in terms of stability, to use the first block of supply-side disaggregation, while demand-side disaggregation tends to be less reliable. This result carries important implications for policymakers aiming to evaluate its short-term effectiveness in both households and firms.

This article explores the determinants of changes to the range of exports at the firm level with a focus on the role played by the firm’s local environment. It extends the model developed by Bernard et al. (2010) to a multiregional setting to account for localized externalities. The model is tested using French micro-data on monoregional manufacturing firms covering the period 2002–2007. Our main finding is that the local product space has an impact on exporters’ product-market entry and exit decisions. Firms tend to modify their exported product mix to achieve congruence with the core products of the locality. Also, firms receive higher revenue from the export of products that are more related to the core capabilities of the locality.

This paper investigates the evolution of monetary transmission mechanism in Malawi between 1981 and 2010 using a time varying parameter vector autoregressive (TVP-VAR) model with stochastic volatility. We evaluate how the responses of real output and general price level to bank rate, exchange rate and credit shocks have changed over timesince Malawi adopted financial reforms in 1980s. The paper finds that inflation, real output and exchange rate responses to monetary policy shocks changed over the period under review. Importantly, beginning mid-2000, the monetary policy transmission performed consistently with predictions of economic theory and there is no evidence of a price puzzle as found in the previous literature on Malawi. However, the statistical significance of the private credit supply remains weak and this calls for more financial reforms targeting the credit market which can contribute to monetary transmission and promote further economic growth in Malawi. JEL classification: C49 D12 D91 E21 E44

The standard approaches to studying industrial agglomeration have been in terms of summary measures of the “degree of agglomeration” within each industry. But such measures often fail to distinguish between industries that exhibit substantially different spatial scales of agglomeration. In a previous paper, Mori and Smith [45] proposed a new pair of quantitative measures for distinguishing both the scale and degree of industrial agglomeration based on an explicit method for detecting spatial clusters. The first, designated as the global extent (GE) of industrial clusters, measures the spatial spread of these clusters (within a given country) in terms of the areal size of their essential containment, defined to be the (convex-solid) region containing the most significant subset of these clusters. The second, designated as the local density (LD) of industrial clusters, measures the spatial extent of individual clusters within their essential containment in terms of the areal share of that containment occupied by clusters. The central purpose of the present paper is to apply these two measures to the manufacturing industries in Japan, and to demonstrate how they can be used in combination to distinguish both the relative scale and degree of agglomeration exhibited by cluster patterns for each industry. In addition, the information provided by this pair of measures (GE, LD) is systematically compared to that of the most prominent summary measures currently in use. Finally, it is shown that these measures also support certain predictions of new economic geography models in the sense that shipping distances for establishments in each industry tend to be negatively (positively) correlated with the GE (LD) measures of agglomeration in these industries.

Starting from a new continuous-time non-linear dynamic model of the exchange rate, we formally show that the introduction of a Tobin tax reduces speculators’ profit and influences the dynamics of the system, making it more stable and less prone to chaotic motion.

The overall operational reputation of a port is based on objective factors, including infrastructure endowments and efficiency in the logistics chain as well as on perceived subjective factors such as reliability, and level of corruption. In this work we analyze the concept of port attractiveness, starting with the hypothesis that subjective port determinants (i.e., user perception) and objective/endogenous and exogenous factors can be quantified together. We thus determine the Port Attractiveness Index and test it using 41 container ports of 23 African countries for the period 2006-2010. We apply a bottom-up ap-proach to investigate the structural relationships among the three sets of deter-minants (endogenous, exogenous and subjective) that impact on port attractive-ness. Our methodological approach employs structural equation modeling. Results indicate that subjective factors are indeed influential variables for port attractiveness. Moreover, when examining port attractiveness and investment strategies, we demonstrate that in many cases in African ports governments should implement soft infrastructure as a first step rather than investing in hard infrastructures.

We have shown that in order to make a decision regarding the optimal formula for Romania’s regionalization it is necessary to consider more than one criteria. The aim of this paper is to study one of these criteria – the industrial sector. Using instruments provided by Onicescu’s informational statistics we analyze the homogeneity of the Romanian regions regarding two variables, the industrial production index and the turnover value index in industry. We find out that from this point of view regions are not homogeneous, which leads to the conclusion that industry is an aspect that can be considered when deciding on regionalization.

If before the outbreak of the great recession in the year 2008 the European labour markets were added almost 30 million new jobs, during the crisis the same markets eliminated six million jobs, and unemployment reached a peak of 11% in 2013, the highest rate in more than a decade.At European level, even though political and economic decision factors took firm measures for alleviating the negative effects of the economic crisis, many economies are still faced with the actual perspective of an extended period of tempered economic growth or even economic decline. The employment of vulnerable labour force and employed population poverty are very important issues needing to be tackled and solved. The statistical methods and techniques of quantification, the factor analysis for estimating and testing are both represented by a wide and varied multitude of procedures and statistic-mathematical instruments. For deepened analysis of the impact of adopting active measures on the evolution of the main macroeconomic indicators which highlight the employment of labour force, the paper used the method of principal components

DSGE are for a time the favorite models in the simulation of monetary policies at the central banks. Two of its basic assumptions are discussed in this paper: (a) the absence of endogenous nonlinearities and the exogenous nature of shocks and (b) the persistence of or the return to equilibrium after a shock, or the absence of dynamics. Our analysis of complex financial markets, using historical data of S&P500, suggests otherwise that financial regimes endogenously change and that equilibrium is an artifact.

Economic agents are aware of incurring a loss in basing their decisions on their own extrapolations instead of on sound statistical data, but this loss may be smaller than the one related to waiting for the dissemination of the final data. Broad guidelines on deciding when statistical offices should release preliminary and final estimates of the key statistics may come from comparing the loss attached to users’ predictions with the loss associated to possible preliminary estimates from incomplete samples. Furthermore, the cost of delaying decisions may support the dissemination of very early estimates of economic indicators, even if their accuracy is not fully satisfactory from a strict statistical viewpoint. Analysing the vintages of releases of quarterly Euro area GDP supports the view that even very inefficient predictions may beat some official preliminary releases of GDP, suggesting that the current calendar of data dissemination requires some adjustment. In particular, actual “flash” estimates could be anticipated, while some later intermediate releases are likely less informative for the users.

What can we, as users of microdata, formally guarantee to the individuals (or firms) in our dataset, regarding their privacy? We retell a few stories, well-known in data-privacy circles, of failed anonymization attempts in publicly released datasets. We then provide a mostly informal introduction to several ideas from the literature on differential privacy, an active literature in computer science that studies formal approaches to preserving the privacy of individuals in statistical databases. We apply some of its insights to situations routinely faced by applied economists, emphasizing big-data contexts.

Calculating accurately the optimal hedge ratio plays an important role in the futures market for both practitioners and academicians. In this paper, we combine copula and nonparametric technique, where marginal setting is modeled by nonparametric technique and bivariate is linked by dynamic Patton (2006)'s SJC copula function, to estimate the parameters of optimal hedge ratio. Various types of GARCH models to fit the marginal distribution are also compared. Furthermore, model specification for marginal setting is investigated by Hong and Li (2005)'s statistics, which test the i.i.d. and U(0,1) simultaneously. The empirical results show that transformed residuals generated by nonparametric technique are i.i.d. U(0,1), while most of one generated by popular GARCH-type are not. For hedging effectiveness, our methods perform better than traditional copula-GARCH models. The robust test also supports the results.

Korhonen and Peresetsky (2013) suggested a new Kalman-filter type model of financial markets to extract a global stochastic trend from discrete non-synchronous data on daily stock market index returns from different markets. We extend this model to allow the correlation between increments of this global trend on neighbor intervals. Existence of that non-zero correlation is demonstrated. However it does not mean that it helps forecast daily returns of the stock indices itself, since the global stochastic trend is unobservable. Forecasting performance of the model with three stock markets is explored.

Based on two hundred years of annual data of the Netherlands , Germany , US and Japan we analyse the mean reversion of long-term interest rates, by unit root tests over rolling windows and taking into account structural breaks and regime changes. While short-term rates and the yield curve tend to revert to their long-term average value, long-term rates can persistently deviate from it. At the outside, we only find weak statistical evidence for mean reversion of long-term rates. Outcomes of smooth transition autoregressive ( STAR ) models for long-term interest rates, indicate that the speed of mean reversion is regime dependent, being stronger when rates are far from their equilibrium value.

Is it possible to beat the market by mechanical trading rules based on historical and publicly known information? Such rules have long been used by investors and in this paper, we test the success rate of trades and profitability of the Open Range Breakout (ORB) strategy. An investor that trades on the ORB strategy seeks to identify large intraday price movements and trades only when the price moves beyond some predetermined threshold. We present an ORB strategy based on normally distributed returns to identify such days and find that our ORB trading strategy result in significantly higher returns than zero as well as an increased success rate in relation to a fair game. The characteristics of such an approach over conventional statistical tests is that it involves the joint distribution of Low, High, Open and Close over a given time horizon.

There are various methods and scales used for determining the deviations of the baby growth. A scale has been applied to 40 babies in the 12, 24 and 36 month periods and language development has been explained by the period of breast feeding without any additional food, social-emotional development during these periods and fine motor development variables. As a sub-model the period of breast feeding without any additional food has been explained by the gender of the baby, the educational status of the mother during birth and the birth weight of the baby. Furthermore other sub-models, the social-emotional development of the baby has been interpreted by the educational status and the age of the mother during birth variables, fine motor development of the baby has been explained by the baby's gender, mother's educational status, baby's birth height and weight variables. Multilevel analysis has been applied to these data which have longitudinal structure.

Segregation and Socio-spatial Fragmentation - The Ordeal of Measurement: This note is a reflection on the meaning and scope of segregation and socio-spatial fragmentation indicators and, beyond, on the role of statistics in sociological reasoning. It shows that if these indicators can be used to test theories of segregation or fragmentation, they must nevertheless themselves be put to the test of sociological reasoning. The choice of statistical indicators is actually not a purely technical exercise but is strongly linked to the meaning we give to the phenomenon. Their construction and interpretation always depend on how it was defined previously, how its causes have been pictured, and its consequences have been anticipated. In this sense, statistical indicators can only be a step in a particular argument.

The most important properties of p-variate distributions on the hypercube whose univariate marginals are uniformly distributed on [0; 1] are discussed. These distributions, also called p-copulas, have become a popular tool in order to study financial markets, macroeconomics and other fields. The study of Russian articles shows that in most cases these articles contain a list of several typical copulas and techniques of their using but they hold no discussions about meaning of acts over copulas. The review is an attempt to change for the better this situation, even if it were a little.

This study examines the theme of female leadership as a complex concept and an embedded phenomenon. It is in a feminist paradigm that attempts to give voice to women, and works to correct the imbalance generally posited from a predominantly male oriented perspective. In this instance, a case study approach is considered entirely appropriate since it dealt with a process of complex real-life activities in considerable depth. This paper aims to establish the case study approach as a strategic qualitative research methodology in the female leadership field. It discusses the seven steps employed in a case study approach, namely: (1) Justification for the research paradigm and research methodology, (2) Justification for the case study method, (3) Criteria for judging the quality of case study design (4) Designing the case study, (5) Criteria for selecting a case design, (6) Data collection, (7) Case study analysis. While this article reviews the case study approach used to investigate female leadership in Thailand, it undoubtedly will also have application elsewhere.

One of the indirect statistical methods for evaluating the asymmetry of the data between partner countries in the realization of foreign trade activity is the so - called method of „mirror compar isons”. Its use permits the bringing out „in the daylight” a large part of contraband import and export, whereby in the budget of the country there will enter considerable financial resources in the form of taxes, duties and excise, and there will be put a n end to the bad practice of wrongful refunding of value added tax. From that viewpoint the bilateral mirror international comparisons constitute a serious anticrisis measure and ought to be introduced as regular practice in the activities of the authorities of the Customs Houses Agency, the National Revenue Agency, the National Statistical Institute and other interested state institutions. In the article there are systematized the reasons for the presence of asymmetry in the data on foreign trade between partner countries and is substantiated the necessity for regular conducting of mirror comparisons, as is offered an algorithm for their practical application.

Partial Least Squares Regression (PLSR) is a multivariate statistical method for constructing predictive models when the variables are many and highly collinear. Its goal is to predict a set of response variables from a set of predictor variables. This prediction is achieved by extracting a set of orthogonal factors called latent variables from the predictor variables. This study investigated the performances of model selection criteria in selecting the optimum number of latent variables from PLSR models for data sets that have various observations and variable numbers. Their performances have been compared in a simulation study with k-fold cross validation. This simulation has been performed to compare the performance of MAIC (Bedrick & Tsai, 1994), MAIC (Bozdogan, 2000), MA_opt(PRESS) and Wold’s R criterion in finding the optimum number of latent variables. The simulation results show that all the criteria achieved the optimum number of latent variables for a small-sized design matrix. But when the data dimensions get bigger, MAKAKIE and MBEDRICK could not find the optimum number of latent variables. MA_opt(PRESS) and Wold’s R criteria gave almost the same results and found the optimum number of latent variables with a better performance than the MAIC’s.

This article contains the second part of the consultation series on copula functions and their use in modeling multidimensional probability distributions. It describes pair-copula functions (including the concept of canonical and D-vines), alternative measures of dependence useful to summarize the dependence structure of the analyzed variables (including measures of tail dependence, particularly relevant in the case of asymmetric distributions), as well as parametric, semi-parametric and nonparametric methods of statistical estimation of copula functions.

Bemutatjuk a többállapotú periódustermékenységi tábla módszerét, és ezt alkalmazva a magyar születési rátákra, először számítjuk ki a magyar népesség kor- és paritásfüggő teljes termékenységi arányszámait - visszamenőleg az elmúlt 40 évre. A számítás döntő jelentőségű részletekkel is szolgál: azt is megadja, hogy az anya egyes korévei és paritásai hány gyermekkel járulnak hozzá a teljes termékenységi arányszámhoz. Ezzel a várható gyermekszámon túl előáll egy családban a gyermekek számának kor és paritás szerinti várható megoszlása, ami már lehetővé teszi a termékenység alakulásának dinamikai vizsgálatát. A módszerrel előállíthatók más mutatók is, ilyen például az anyák átlagos életkora születési sorszám szerint vagy a születések közötti várható időintervallum. A tanulmány második részében dekompozíciós eljárást alkalmazva, kor- és paritásfüggő teljes termékenységi arányszámbeli összehasonlításokat végzünk a jelen és különböző múltbeli időpontok, valamint az egyes régiók magyar népességei között, továbbá nemzetközi egybevetéseket is teszünk. Journal of Economic Literature (JEL) kód: J13, C49., The paper presents the method of a several-state fertility table over time and applies it to Hungarian birth rates, first calculating in retrospect the full age and par-ity-dependent fertility ratios for the Hungarian population over the last forty years. The calculation provides some details of decisive importance, including the number of children contributed to the full fertility ratio by individual age groups and parities of the mother. This provides not only the likely number of children, but the likely distribution of the number of children by age and parity in a family, which allows a dynamic examination of the fertility tendency to be made. The same method can be applied to other indicators as well, for instance the average age of mothers at the time of successive births or the likely intervals between births. The second part of the study applies a decomposition process to produce full age and parity-dependent fertility comparisons for the present and for various past moments in time. Regional comparisons within Hungary and international comparisons are also made.

Problems which are related to copula functions, their properties, selection methods for specific baseline data, evaluation, and possible applications are extremely sparingly discussed in the world literature, and are almost not discussed at all in the Russian literature. At the same time, we already had impressive examples of their applications in situations when the construction, statistical estimation and analysis of multidimensional probability distributions turn out to be an essential tool of applied research, and the use of the multivariate normal (Gaussian) distributions for these purposes does not reflect the specific features of the available data. There are grounds to argue that models which are based on copula functions will be in particular demand for applied econometric studies regarding problems of assessment, analysis and management of financial and insurance risks, as well as the returns of various financial instruments. The material proposed in this issue of the journal is, in fact, a fragment of the forthcoming textbook «Methods of econometrics. Advanced level» by S. A. Aivazian, D. Fantazzini

The final part of the consultation series on copula functions is devoted to the description of copula selection methods to choose the copula model that provides the best fit for the empirical data at hand, as well as to the description of copula evaluation methods by using goodness-of-fit tests.

The findings presented in this article improve the existing methods for estimating financial models of returns and especially for estimating the parameters that are relevant for portfolio managers, such as the Jensen alpha, a popular measure for stock selection, and beta, a well-known systemic risk measure. The authors focus on the presence of measurement errors in these models. For instance, the risk factors in the Fama and French models or the market model are biased by measurement, or specification, errors. These measurement errors are related to the use of proxies for measuring risk factors, such as the risk premium, and other risk factors, which are approximated by mimicking portfolios. To tackle these specification problems, the authors propose new Hausman-based estimators lying on cumulants optimal instruments. Using these newly generated strong instruments in a generalized method of moments (GMM) setting, the authors obtain new GMM estimators that they call GMM-C and its homologue GMM-hm. They also extend the methodology to the standard two-stage least squares (TSLS) framework using new optimally generated instruments. These procedures improve the existing method of moments for estimating, or calibrating, the parameters of a financial model and, more generally, for treating the problem of endogeneity often encountered in financial studies. Moreover, this study leads to a new indicator that signals the presence of specification errors in financial models. The authors apply a battery of tests and estimators to a sample of 22 HFR hedge fund indices observed monthly over the period 1990–2005. Their tests reveal that specification errors bias parameter estimation of financial models of returns and that the ranking of hedge funds is very sensitive to the choice of estimators.

A method of analysis of Time Series Internal Structures based on Singular Spectrum Analysis is discussed. It has been shown that in the case when the Time Series contains deterministic additive components rank of the trajectory matrices equal to number of parameters of the components. Also it was proved that both eigen and factor vectors repeat shapes of the additive components and both eigen values and eigen vectors can be divided into additive groups. Some useful patterns of deterministic components were identified, which permit to provide graphical analysis of times series Internal Structures.

We present some reflections upon the relation between mathematics and the development of the modern society from the perspective of the ideas of the great Romanian encyclopaedist Dimitrie Cantemir.

This study insists on presenting the applicability of the multicriterial programming in the economic activity. Because the choosing of the final alternative is based on generating the set of nondominated points algorithm I created the software application package MCRIT (developed in C++) which allow us to generate the Nex set and also the choosing of final alternative using the method of ideal solution. The program was tested on small, medium and large systems and it allow us to obtain the set of nondominated points in a reasonable amount of time. This paper presents a case study which was solved with MCRIT package. Because the Nex set is to large I propose to limit it by imposing an acceptable threshold (an acceptable distance by the ideal solution) to that the variants are accepted by the decision maker. So became efficiently the use of ELECTRE method to determine the final solution. Moreover, besides the fact that the decision maker is moving in the space of reasonable solutions, it also could considerate the qualitative criterions which accompanying the reality of model and which couldn’t be surprised by the mathematical programming.

El objetivo de este artículo es el análisis de la situación actual y de las perspectivas a corto y mediano plazos de la industria maquiladora de exportación en el Estado de México. Se trata de efectuar un análisis de coyuntura económica cuantitativa, combinando convenientemente los elementos necesarios para ello, esto es, la base empírica (información estadística relevante en forma de series temporales mensuales del valor agregado de exportación cobrado por el servicio de maquila –VAECSM– el Estado de México) y los métodos cuantitativos (conjunto de técnicas estadístico-econométricas). Para ello se presenta una propuesta metodológica que se apoya en la modelización ARIMA univariante de las series emporales, complementa a con el análisis de outliers y el efecto calendario, y la extracción de señales de dichas series, mediante el procedimiento de modelos de forma reducida. El componente tendencia-ciclo se erige como la señal adecuada para analizar la evolución subyacente de las series. A partir de la misma, y de los conceptos de crecimiento subyacente e inercia, podemos extraer las principales conclusiones respecto a la situación coyuntural del sector de la maquila del Estado de México, detallando además las mismas a nivel regional.DOI: http://dx.doi.org/10.5377/universitas.v3i2.1666

La decisión de la localización es una de las opciones estratégicas más importantes que tienen que tomar las empresas, con influencia directa sobre su competitividad. Por este motivo, se han desarrollado numerosas metodologías que ayudan en la toma de estas decisiones. En este trabajo, se realiza una aplicación empírica de una metodología de ayuda a la decisión multicriterio; específicamente, se ha optado por el método ELECTRE en sus versiones I y III, con el fin de seleccionar el mejor emplazamiento para una empresa. Para esta aplicación, se utilizan como alternativas de localización las Comarcas de Aragón, como factores de localización se utilizan 8 criterios y como peso o importancia de esos factores los obtenidos mediante una encuesta. Además, se aporta evidencia sobre la consistencia de los resultados obtenidos. Como se aprecia en el trabajo, aunque la tipología de los problemas a los que van destinados ambos métodos son distintos, los resultados obtenidos pueden ser considerados como consistentes. = The location decision is one of the most important strategic decisions that have to take the firms, with direct influence on their competitiveness. For this reason, many methodologies have been developed to help in the decision making. In this paper, we make an empirical application of a multi-criterion methodology of aid to the decision; specifically, ELECTRE method has been chosen in its versions I and III, in order to select the best place for a firm. The alternatives are counties in Aragon (Spain), and we use eight criteria. The weight has been obtained by surveys. In addition, evidence is contributed about the consistency of the obtained results. Though the typology of the problems to which both methods are destined are different, the obtained results can be considered consistent.

We develop a new approach for decision making in educational management based on the use of distance measures. We focus on the selection of a studies plan from the perspective of an academic institution. We try to develop this approach showing the benefits of establishing an ideal plan that we compare with the available alternatives. We use the Minkowski distance, the ordered weighted averaging (OWA) operator and the interval numbers. The use of the Minkowski distance allows to make comparisons between the ideal plan and the available ones in the market. The OWA operator is an aggregation operator that provides a parameterized family of aggregation operators that includes the maximum, the minimum and the average criteria, among others. And the interval numbers is a very useful technique to represent the information when the environment is very complex, because it gives all the possible results from the minimum to the maximum. We introduce a new aggregation operator called the uncertain generalized ordered weighted averaging distance (UGOWAD) operator. It is a distance aggregation operator that uses the main characteristics of the Minkowski distance, the OWA operator and the interval numbers. We develop an illustrative example where we can see the usefulness of the UGOWAD operator to select a studies plan in education management. The main advantage of using the UGOWAD is that we can consider a wide range of distance aggregation methods in the decision problem. Then, the decision maker gets a more complete view of the decision problem, being able to select the alternative that better fits the interests. Se desarrolla un nuevo modelo para la toma de decisiones en procesos de gestión de la educación basados en las medidas de distancia. El análisis se enfoca en analizar un proceso de selección de plan de estudios desde la perspectiva de una institución académica. Se intenta mostrar la practicidad de utilizar un plan de estudios imaginario que sería el ideal a partir del cual se compararían las diferentes alternativas disponibles. Para realizar esto, se utilizarán diferentes técnicas disponibles en Teoría de la Decisión, como son la distancia de Minkowski, el operador de medias ponderadas (OWA) y los intervalos de confianza. La utilización de la distancia de Minkowski nos permite hacer comparaciones entre un plan de estudios ideal y los disponibles en la realidad. El operador OWA es un operador de agregación que proporciona una familia parametrizada de operadores de agregación entre los cuales se destaca el máximo, el mínimo y la media aritmética. Los intervalos de confianza son de gran utilidad para representar la información cuando el entorno es muy complejo, porque proporciona todos los resultados que se podrían producir desde un mínimo hasta un máximo. Por eso, incluye todos los posibles resultados que se pueden producir. Para realizar esto, se introduce un nuevo operador de agregación denominado como el operador de distancia media ponderada ordenada generalizada incierta (UGOWAD o UMOWAD). Es un operador de agregación de distancias que utiliza las principales características de la distancia de Minkowski, del operador OWA y de los intervalos de confianza. Se desarrolla un ejemplo ilustrativo en donde se puede ver la utilidad del operador UGOWAD para la selección de un plan de estudios en la gestión de la educación. La principal ventaja de utilizar el operador UGOWAD está en poder considerar una amplia gama de operadores de agregación de distancias en el problema decisional. Entonces, el decisor obtiene una visión mucho más completa del problema y está capacitado para seleccionar la alternativa que se acerca más a sus intereses.

The aim of the paper is to analyze the current situation and perspectives, both at the short run andmiddle run, of theMexican exportationmaquiladora industry. It is to combine quantitative economic conjuncture analysis with its principal components, that is, the empiric base (statistical information in monthly time series, in this case Mexican aggregated value of exports by maquila services in total national and regional levels in Mexico) and the quantitative methods (statistical-econometrics techniques). For this purpose we present a methodological proposal thatmodels the time series through ARIMAmodels with outliers and calendar effects, and next we use a reducedmodel-based procedure for the signal extraction of these time series. The trend cycle component is the appropriate way to analyze its underlying evolution. Using the concepts of underlying growth and inertia,we can extract themain conclusions for the current situation of the Mexican maquila sector, including the regional level.

This paper addresses whether and to what extent econometric methods used in experimental studies can be adapted and applied to financial data to detect the best-fitting preference model. To address the research ques- tion, we implement a frequently used nonlinear probit model in the style of Hey and Orme (1994) and base our analysis on a simulation study. In detail, we simulate trading sequences for a set of utility models and try to identify the underlying utility model and its parameterization used to generate these sequences by maximum likelihood. We find that for a very broad classification of utility models, this method provides acceptable outcomes. Yet, a closer look at the preference parameters reveals several caveats that come along with typical issues attached to financial data, and that some of these issues seems to drive our results. In particular, deviations are attributable to effects stem- ming from multicollinearity and coherent under-identification problems, where some of these detrimental effects can be captured up to a certain degree by adjusting the error term specification. Furthermore, additional uncertainty stemming from changing market parameter estimates affects the precision of our estimates for risk preferences and cannot be simply remedied by using a higher standard deviation of the error term or a different assumption regarding its stochastic process. Particularly, if the variance of the error term becomes large, we detect a tendency to identify prospect theory as utility model provid- ing the best fit to simulated trading sequences. We also find that a frequent issue, namely serial correlation of the residuals, does not seem to be an im- portant issue. However, we detected a tendency to prefer nesting models over nested utility models, which is particularly prevalent if rank-dependent utility and exponential power utility models are estimated along with expected utility with constant relative risk aversion utility models. [Last revised: 12 Apr 2017]

We analyse the survival characteristics of recordings that reached the number one spot on the US popular music charts over the period 1955 to 2003. Our results show that there has been a statistically significant change in the time spent at number one since ‘album cuts’ were included in the compilation of Billboard 's Hot 100. Survival time is significantly improved if the recording is by a female solo artist, or if it is an instrumental tune. We also find a significant ‘Elvis effect’. ‘I’ll never be a saint, it's true. I’m too busy surviving!’ (Madonna, 1994)

The aim of this study is to investigate the relation between the demographic features and the attitude devoted to the statistics lesson of the students in departments of business administration, economics and public finance that will be able to effective on the socio- economic structure of our country in the future. By the purpose of measuring the attitude devoted to the statistics, Survey of Attitudes Towards Statistics and a demographic knowledge form prepared by the researchers are used in the stage of data collection. The Nonlinear Canonic Correlation Analyze is used for put forward the relation between the accomplishment note in the Statistics lesson, repetition number, repetition reason and the attitude devoted to the statistics lesson of the university students. In the conclusion of the analysis it was found that the variables of accomplishment note in the Statistics lesson, repetition number and repetition reason are perform on the attitudes of students toward the Statistics lesson.

This paper exhibits the economic taxonomy of a minimal spanning tree constructed from the correlation matrix of daily returns of 65 stocks traded at the Bolsa Mexicana de Valores during an eight-year trading period. This paper also makes a classification of the risk and returns of these stocks depending on their distances in the tree to the central vertex (mass center)

In the paper, an Analysis of Principal Components is used to explain the structure of Variance and Covariance, associated with the variables that represent the Mexican industrial performance, from the financial point of view in the period 1998-2001, with the purpose of reducing the number of independent variables, and to obtain some variables "artificial" or factors, in form of lineal combinations of the original variables, so that they reflect, in more efficient way, the variability of the group.

This paper first applies the MODWT (Maximal Overlap Discrete Wavelet Transform) to Euro Area quarterly GDP data from 1995 – 2014 to obtain the underlying cyclical structure of the GDP components. We then design optimal fiscal and monetary policy within a large state-space LQ-tracking wavelet decomposition model. Our study builds a MATLAB program that simulates optimal policy thrusts at each frequency range where: (1) both fiscal and monetary policy are emphasized, (2) only fiscal policy is relatively active, and (3) when only monetary policy is relatively active. The results show that the monetary authorities should utilize a strategy that influences the short-term market interest rate to undulate based on the cyclical wavelet decomposition in order to compute the optimal timing and levels for the aggregate interest rate adjustments. We also find that modest emphasis on active interest rate movements can alleviate much of the volatility in optimal government spending, while rendering similarly favorable levels of aggregate consumption and investment. This research is the first to construct joint fiscal and monetary policies in an applied optimal control model based on the short and long cyclical lag structures obtained from wavelet analysis.

Within currency unions, the conventional wisdom is that there should be a high degree of macroeconomic synchronicity between the constituent parts of the union. But this conjecture has never been formally tested by comparing sample of monetary unions with a control sample of countries that do not belong to a monetary union. In this paper we take euro area data, US State macro data, Canadian provincial data and Australian state data — namely real Gross Domestic Product (GDP) growth, the GDP deflator growth and unemployment rate data — and use techniques relating to recurrence plots to measure the degree of synchronicity in dynamics over time using a dissimilarity measure. The results show that for the most part monetary unions are more synchronous than non-monetary unions, but that this is not always the case and particularly in the case of real GDP growth. Furthermore, Australia is by far the most synchronous monetary union in our sample.

Despite the widespread interest in the topic and a vast international literature, very little is known about the development of intergenerational mobility in Switzerland. Based on a new harmonized database for Switzerland (comprising various surveys such as different waves of the ISSP, EVS, and the ESS), we provide a systematic account of changes in the link between social origin and destination over time (covering birth cohorts from around 1935 to 1980). We analyze effects of parental education and class on own educational achievement and social class for both men and women, using a refined variant of the methodological approach proposed by Jann and Combet (2012). The approach is based on the concept of proportional reduction of error (PRE) and features a number of advantages over more traditional approaches. For example, it provides smooth estimates of changes in social mobility that have a clear interpretation and it can easily incorporate control variables and multiple dimensions of parental characteristics. To evaluate the validity of our approach, we employ the oft-used log-multiplicative layer effect (a.k.a Unidiff) model (Xie 1992, Erikson and Goldthorpe 1992) as a benchmark. Results indicate that our approach performs well and produces qualitatively similar findings as Xie's model. For both men and women, effects of social origin initially decreased, but then, towards the end of the observation period, increased again. This u-shaped pattern, which can be observed with respect to both education and class, appears to be more pronounced for women than for men.

This paper intends to explore the utilization of entropy through football, generalizing the interpretation of entropy. We consider it as a measure of competitiveness of football leagues and relate it to the UEFA ranking, which ranks yearly the performance of countries in European Cups. We expect that more competitive leagues, meaning that a championship is more leveled, have a better UEFA ranking. Correlating entropy with UEFA ranking, we find evidence of that relationship

Important time-budget methodological issues are concerned with analysing time use tables, obtainable from time-budget diaries to face the multipurpose nature, the size and the complexity of time-budget data. After a brief introduction to the main time use analysis the paper focuses on the cross-sectional analysis using the explorative multidimensional data analysis. The paper deals with the multiway methods suitable for comparing statistical studies (i.e. countries) when each of them has many variables (i.e. activities) observed on many cases (i.e. categories of population). This article examines an example of application to cross-national differences in time use in six European countries at different stages of life. The results are exemplary of the applicational steps and statistical aspects of the methods proposed rather than definitive findings.

This paper scrutinizes the publishing histories of associate and tenured professors of economic sciences in the Czech Republic. According to domestic regulation, only certain universities may grant associate and tenured professorships. Such universities require that professorial candidates publish original work in internationally credible academic journals. To investigate whether such publishing requirement is indeed fulfilled, the authors studied the publishing histories of those who were granted tenured or associate professorships from January 1999 to June 2005 using the Social Sciences Citation Index developed by the Institute of Scientific Information. They found that, of the professors tenured in the respective period, almost 54 percent had not published any journal article before their appointment. At the same time, some 85 percent had not published an article in a foreign academic journal. Similar results held for those who were made associate professors in that period. It appears, then, that Czech universities in fact do not regard publishing as a principal factor on the academic tenure-track in the economic sciences.

We characterize asset return linkages during periods of stress by an extremal dependence measure. Contrary to correlation analysis, this nonparametric measure is not predisposed toward the normal distribution and can allow for nonlinear relationships. Our estimates for the G-5 countries suggest that simultaneous crashes between stock markets are much more likely than between bond markets. However, for the assessment of financial system stability the widely disregarded cross-asset perspective is particularly important. For example, our data show that stock-bond contagion is approximately as frequent as flight to quality from stocks into bonds. Extreme cross-border linkages are surprisingly similar to national linkages, illustrating a potential downside to international financial integration. Published in Review of Economics and Statistics (2004), 86, 313-326.

Green and Hollifield (1992) argue that the presence of a dominant factor would result in extreme negative weights in mean-variance efficient portfolios even in the absence of estimation errors. In that case, imposing no-short-sale constraints should hurt, whereas empirical evidence is often to the contrary. We reconcile this apparent contradiction. We explain why constraining portfolio weights to be nonnegative can reduce the risk in estimated optimal portfolios even when the constraints are wrong. Surprisingly, with no-short-sale constraints in place, the sample covariance matrix performs as well as covariance matrix estimates based on factor models, shrinkage estimators, and daily data. MARKOWITZ'S (1952, 1959) PORTFOLIO THEORY is one of the most important theoretical developments in finance. Mean-variance efficient portfolios play an important role in this theory. Such portfolios constructed using sample moments often involve large negative weights in a number of assets. Since negative portfolio weights (short positions) are difficult to implement in practice, most investors impose the constraint that portfolio weights should be nonnegative when constructing mean-variance efficient portfolios. Green and Hollifield (1992) argue that because a single factor dominates the covariance structure, it would be difficult to dismiss the observed extreme negative and positive weights as being entirely due to the imprecise estimation of the inputs. They consider the global minimum variance portfolio to avoid the effect of estimation error in the mean on portfolio weights. They note that when returns are generated by a single factor model, minimum variance portfolios can be constructed in two steps. First, naively diversify over the set of high beta stocks and

Hrvatske sume d.o.o. (limited liability company) is a state-owned enterprise for forest and woodland management in the Republic of Croatia. The area under its governance provides for annual increment of 9.6 million m3 of gross wood mass, whereas 5.4 million m3 is on a yearly basis. This cut wood mass needs to be transported to designated destinations. Wood mass transport has continuously increased, with 4.8 million m3 transported in 2013. Out of this quantity, the company Hrvatske šume d.o.o. transported 12.8% of wood mass using its own facilities, and the rest was transported by subcontractors. For these reasons, a transportation model can be an important part of operations. The paper aims to analyze transport-related issues in the company Hrvatske šume d.o.o. The insight into the current situation in wood mass transport will provide a basis for the proposed optimization of operations. Key factors of transport will be considered in devising an optimization proposal.

The paper develops an algorithm for making long-term (up to three months ahead) predictions of volatility reversals based on long memory properties of financial time series. The approach for computing fractal dimension using sequence of the minimal covers with decreasing scale (proposed in [1]) is used to decompose volatility into two0dynamic components: specific A (t ) and structural Hµ(t ). We introduce two separate models forA (t ) and Hµ(t ), based on different principles and capable of catching long uptrends in volatility. To test statistical significanceof its abilities we introduce several estimators of conditional and unconditional probabilities of reversals in observed and predicted dynamic components of volatility. Our results could be used for forecasting points of market transition to an unstable state.