Les registres de l’action syndicale européenne

This study of how trade union actions have been shaped at the EU level is based on an analysis of the archives of the European Trade Union Confederation. It sheds light on a historical process that has fostered four types of action that now organize European trade union representations. The gradual formation of these types is described while pointing out the causes and explaining their rationales. Emphasis is placed on the complex process whereby institutions have shaped the thinking and acting of trade unions at the EU level. The way that unions form a framework for their practices has effects back upon the shaping of the collective organization.     

Random alliance networks

The morphological properties of kinship and marriage alliance networks, such as circuits, are typically considered as indicators of sociological phenomena — yet, they may also be partly coincidental. To assert the contribution of chance to these morphological features, we develop a standardized method where empirical alliance networks are compared with a random baseline. We apply our framework to a variety of empirical cases and show that some corpuses are remarkably well reconstructed by our random model, while others still feature significant divergencies which may be partly connected to field-based experience. On the whole, our approach may be used to scrutinize the matrimonial role of social groups as asserted by native or ethnological theory.     

Particle filtering for partially observed Gaussian state space models

Summary. Solving Bayesian estimation problems where the posterior distribution evolves over time through the accumulation of data has many applications for dynamic models. A large number of algorithms based on particle filtering methods, also known as sequential Monte Carlo algorithms, have recently been proposed to solve these problems. We propose a special particle filtering method which uses random mixtures of normal distributions to represent the posterior distributions of partially observed Gaussian state space models. This algorithm is based on a marginalization idea for improving efficiency and can lead to substantial gains over standard algorithms. It differs from previous algorithms which were only applicable to conditionally linear Gaussian state space models. Computer simulations are carried out to evaluate the performance of the proposed algorithm for dynamic tobit and probit models.     

A New Approach to Volatility Modeling: The Factorial Hidden Markov Volatility Model

A new process—the factorial hidden Markov volatility (FHMV) model—is proposed to model financial returns or realized variances. Its dynamics are driven by a latent volatility process specified as a product of three components: a Markov chain controlling volatility persistence, an independent discrete process capable of generating jumps in the volatility, and a predictable (data-driven) process capturing the leverage effect. An economic interpretation is attached to each one of these components. Moreover, the Markov chain and jump components allow volatility to switch abruptly between thousands of states, and the transition matrix of the model is structured to generate a high degree of volatility persistence. An empirical study on six financial time series shows that the FHMV process compares favorably to state-of-the-art volatility models in terms of in-sample fit and out-of-sample forecasting performance over time horizons ranging from 1 to 100 days. Supplementary materials for this article are available online.     

Using Adjacent-category Logits Procedure for Estimating Receiver Operating Characteristic Surface

This article presents a semiparametric method for estimating receiver operating characteristic surface under density ratio model. The construction of the proposed method is based on the adjacent-category logit model and the empirical likelihood approach. A bootstrap approach for the VUS estimator inference is presented. In a simulation study, the proposed estimator is compared with the existing parametric and nonparametric estimators in terms of bias, standard error, and mean square error. Finally, a real data example and some discussions on the proposed method are provided.     

Sparse Change-point HAR Models for Realized Variance

Change-point time series specifications constitute flexible models that capture unknown structural changes by allowing for switches in the model parameters. Nevertheless most models suffer from an over-parametrization issue since typically only one latent state variable drives the switches in all parameters. This implies that all parameters have to change when a break happens. To gauge whether and where there are structural breaks in realized variance, we introduce the sparse change-point HAR model. The approach controls for model parsimony by limiting the number of parameters which evolve from one regime to another. Sparsity is achieved thanks to employing a nonstandard shrinkage prior distribution. We derive a Gibbs sampler for inferring the parameters of this process. Simulation studies illustrate the excellent performance of the sampler. Relying on this new framework, we study the stability of the HAR model using realized variance series of several major international indices between January 2000 and August 2015.     

Autoregressive Moving Average Infinite Hidden Markov-Switching Models

Markov-switching models are usually specified under the assumption that all the parameters change when a regime switch occurs. Relaxing this hypothesis and being able to detect which parameters evolve over time is relevant for interpreting the changes in the dynamics of the series, for specifying models parsimoniously, and may be helpful in forecasting. We propose the class of sticky infinite hidden Markov-switching autoregressive moving average models, in which we disentangle the break dynamics of the mean and the variance parameters. In this class, the number of regimes is possibly infinite and is determined when estimating the model, thus avoiding the need to set this number by a model choice criterion. We develop a new Markov chain Monte Carlo estimation method that solves the path dependence issue due to the moving average component. Empirical results on macroeconomic series illustrate that the proposed class of models dominates the model with fixed parameters in terms of point and density forecasts.     

Academic Paths,Ageing and the Living Conditions of Students in the Late 20th Century*

Le monde étudiant est profondément marqué par la diversité des tra‐jectoires individuelles, qui sont souvent très éloignées d'un déroule‐ment linéaire des études. Les conséquences sur l'âge des étudiants sont majeures, et la condition étudiante ne peut plus être définie comme une expérience strictement juvénile. Or, entre 20 et 30 ans, l'âge engendre des impératifs différentiels sur le plan des conditions et des modes de vie qui ne sont pas toujours compatibles avec la condition étudiante classique. Cette étude des parcours et de la situation financière des étudiants des universités québécoises de langue françhise et anglaise montre comment s'opère la déconnexion entre jeunesse et condition étudiante, et comment cette déconnexion influe sur la différenciation des conditions de vie et de financement des études. Student life is profoundly marked by the diversity of individual trajectories, which are in stark contrast with the linear path traditionally taken by students. The impact on the age of the student population is significant: indeed, student life can no longer be qualified as strictly for the young. Between the ages of 20 and 30 years, different imperatives come into play in terms of living conditions and lifestyle. These imperatives are not always compatible with the conditions of classic student life. This study of the academic paths and the financial situation of Quebec university students shows how the disconnection between student condition and youth occurs and how this disconnection impacts the differentiation of student's living conditions and modes of financing university studies.     

An avian urban morphotype: how the city environment shapes great tit morphology at different life stages

Urban Ecosystems - Urbanization is a worldwide phenomenon associated with tremendous modifications of natural habitats. Understanding how city dwelling species are affected by those changes is...     

On sequential Monte Carlo sampling methods for Bayesian filtering

In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed. We show in particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature; these lead to very effective importance distributions. Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of the analytic structure present in some important classes of state-space models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.