Relative Income, Inequality and Subjective Wellbeing: Evidence for Brazil

This paper analyzes the determinants of the self-reported income and food sufficiency in Brazil, highlighting how these subjective measures of wellbeing are affected by relative conditions. Analyses are based on pooled data from the 2002/2003 and 2008/2009 Brazilian Family Budget Survey and on estimates of the multinomial relation between income and food sufficiency with absolute and relative indicators. The paper highlights that living in a more affluent neighborhood does matter in self-reported perceptions of wellbeing. Stated income sufficiency depends positively on absolute family income and negatively on relative neighbor income. Moreover, neighborhood education and regional inequality affect both stated perceptions of income and food sufficiency. The paper concludes that improving personal income and education, as well as reducing inequality, is one of the most effective ways to improve both the quality of life and the general perceptions of wellbeing in society.     

Permanently Provisional: History,Facts & Figures of Portuguese Immigration in Switzerland

This article provides an overview of the main demographic, social, and economic characteristics of Portuguese immigration in Switzerland. Firstly, it outlines the political and economic underpinnings of Portuguese migration flows to Switzerland. Then, it analyzes the demographic and sociological composition of these flows, the composition of the Portuguese population in Switzerland, as well as processes of economic and cultural integration. It is notably argued that the enduring Portuguese “ideology of return migration” has played a central role in the patterns of integration of Portuguese immigrants in Switzerland.     

Combinatorial design of a minimum cost transfer line

The problem of equipment selection for a production line is considered. Each piece of equipment, also called unit or block, performs a set of operations. All necessary operations of the line and all available blocks with their costs are known. The difficulty is to choose the most appropriate blocks and group them into (work)stations. There are some constraints that restrict the assignment of different blocks to the same station. Two combinatorial approaches for solving this problem are suggested. Both are based on a novel concept of locally feasible stations. The first approach combinatorially enumerates all feasible solutions, and the second reduces the problem to search for a maximum weight clique. A boolean linear program based on a set packing formulation is presented. Computer experiments with benchmark data are described. Their results show that the set packing model is competitive and can be used to solve real-life problems.     

On the approximability of Time Disjoint Walks

Journal of Combinatorial Optimization - We introduce the combinatorial optimization problem Time Disjoint Walks (TDW), which has applications in collision-free routing of discrete objects (e.g.,...     

Modeling nonlinear time series with local mixtures of generalized linear models

The authors consider a novel class of nonlinear time series models based on local mixtures of regressions of exponential family models, where the covariates include functions of lags of the dependent variable. They give conditions to guarantee consistency of the maximum likelihood estimator for correctly specified models, with stationary and nonstationary predictors. They show that consistency of the maximum likelihood estimator still holds under model misspecification. They also provide probabilistic results for the proposed model when the vector of predictors contains only lags of transformations of the modeled time series. They illustrate the consistency of the maximum likelihood estimator and the probabilistic properties via Monte Carlo simulations. Finally, they present an application using real data.     

Prediction of record values

Given a number of record values from independent and identically distributed random variables with a continuous distribution function F, our aim is to predict future record values under suitable assumptions on the tail of F. In this paper, we are primarily concerned with finding reasonable tolerance regions for future record values. Two methods are proposed. The first one deals with the case where we observe only record values. The second one makes use of the information brought by the complete sample.     

Test of Normality Against Generalized Exponential Power Alternatives

The family of symmetric generalized exponential power (GEP) densities offers a wide range of tail behaviors, which may be exponential, polynomial, and/or logarithmic. In this article, a test of normality based on Rao's score statistic and this family of GEP alternatives is proposed. This test is tailored to detect departures from normality in the tails of the distribution. The main interest of this approach is that it provides a test with a large family of symmetric alternatives having non-normal tails. In addition, the test's statistic consists of a combination of three quantities that can be interpreted as new measures of tail thickness. In a Monte-Carlo simulation study, the proposed test is shown to perform well in terms of power when compared to its competitors.