Estimation for seasonal fractional ARIMA with stable innovations via the empirical characteristic function method

Seasonal fractional ARIMA (ARFISMA) model with infinite variance innovations is used in the analysis of seasonal long-memory time series with large fluctuations (heavy-tailed distributions). Two methods, which are the empirical characteristic function (ECF) procedure developed by Knight and Yu [The empirical characteristic function in time series estimation. Econometric Theory. 2002;18:691–721] and the Two-Step method (TSM) are proposed to estimate the parameters of stable ARFISMA model. The ECF method estimates simultaneously all the parameters, while the TSM considers in the first step the Markov Chains Monte Carlo–Whittle approach introduced by Ndongo et al. [Estimation of long-memory parameters for seasonal fractional ARIMA with stable innovations. Stat Methodol. 2010;7:141–151], combined with the maximum likelihood estimation method developed by Alvarez and Olivares [Méthodes d'estimation pour des lois stables avec des applications en finance. Journal de la Société Française de Statistique. 2005;1(4):23–54] in the second step. Monte Carlo simulations are also used to evaluate the finite sample performance of these estimation techniques.     

Corporate social responsibility and governance

Journal of Management and Governance -     

Is there a global super-bourgeoisie?

In recent decades, accelerating processes of globalization and an increase in economic inequality in most of the world's countries have raised the question of the emergence of a new bourgeoisie integrated at the global level, sometimes described as a global super-bourgeoisie. This group would be distinguished by its unequaled level of wealth and global interconnectedness, its transnational ubiquity and concentration in the planet's major global cities, its specific culture, consumption habits, sites of sociability and shared references, and even by class consciousness and capacity to act collectively. This article successively discusses how the social sciences have examined these various dimensions of the question and begun to provide systematic empirical answers.     

Models of the Joint Structure of Domain-Related and Global Distress: Implications for the Reconciliation of Quality of Life and Mental Health Perspectives

Research on subjective wellbeing includes studies of both domain-related and global distress. The mental health literature, though, focuses almost exclusively on global distress. This seems to be partly due to a common belief that psychological distress, and the moods that comprise distress, necessarily lack referential content. However, if that were the case it would make little sense for any study to ever focus on domain-related distress. The research presented in this report clarifies the relation between global and domain-related distress. We compare confirmatory factor analytic models of the joint relationship among symptoms of global distress with affective symptoms of distress about work, home, and physical appearance. Data are from a general population telephone survey. In the best-fitting models domain-related distress and global psychological distress are related but distinct latent variables. We discuss the theoretical and methodological implications of the models, and model choice.     

« Comment ? Vous n’avez pas de projet ? » : ethnographie du démarchage en matière de crédit à la consommation

Many a product and service is proposed to consumers who have not individually expressed any prior interest to a salesperson or a firm. Two situations of prospecting people for a revolving credit are presented. Based on data gathered during a month of observation in the company of salespersons who deal in this type of credit, the processes are analyzed that these persons use to arouse a potential buyer's interest. Light is shed on the organizational, material and technical conditions that enable them to turn the latter into a customer or, at least, to make interactions with him/her last longer. This analysis of one form of mediation in the marketing of credit also sheds light on the situations of those who subscribe to loans, whether or not they are over indebted.     

Sequential Monte Carlo for rare event estimation

This paper discusses a novel strategy for simulating rare events and an associated Monte Carlo estimation of tail probabilities. Our method uses a system of interacting particles and exploits a Feynman-Kac representation of that system to analyze their fluctuations. Our precise analysis of the variance of a standard multilevel splitting algorithm reveals an opportunity for improvement. This leads to a novel method that relies on adaptive levels and produces, in the limit of an idealized version of the algorithm, estimates with optimal variance. The motivation for this theoretical work comes from problems occurring in watermarking and fingerprinting of digital contents, which represents a new field of applications of rare event simulation techniques. Some numerical results show performance close to the idealized version of our technique for these practical applications.