Dans cet article, nous proposons des tests sur la forme de la distribution des erreurs dans un modèle de régression linéaire multivarié (RLM). Les tests que nous développons sont fonction des résidus obtenus par moindres carrés multivariés, lesquels sont standardisés de façon à ce que...

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian...

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the framework of...

In this paper, we propose exact inference procedures for asset pricing models that can be formulated in the framework of a multivariate linear regression (CAPM), allowing for stable error distributions. The normality assumption on the distribution of stock returns is usually rejected in...

This paper proposes a new nonparametric test for conditional independence, which is based on the comparison of Bernstein copula densities using the Hellinger distance. The test is easy to implement because it does not involve a weighting function in the test statistic, and it can be applied in...

A general formulation of Mixed Proportional Hazard models with K random effects is provided. It enables to account for a population stratified at K different levels. We then show how to approximate the partial maximum likelihood estimator using an EM algorithm. In a Monte Carlo study, the...

We use a multivariate hazard model to analyse the ratification behaviour of ILO conventions by developing countries. The model accounts for two random effects: one at the country level, the other at the convention level. After investigating identification, we use a semi-parametric Bayesian...

Cet article est une revue de la littérature où le temps passé dans un état est une variable aléatoire issue d’un mélange continu de distributions. Elle s’est constituée à partir de l’estimation de fonctions de hasards et de méthodes d’approximations d’intégrales. Nous...

Stochastic volatility models, aka SVOL, are more difficult to estimate than standard time-varying volatility models (ARCH). Advances in the literature now offer well tested estimators for a basic univariate SVOL model. However, the basic model is too restrictive for many economic and finance...

En estimation bayésienne, lorsque le calcul explicite de la loi a posteriori du vecteur des paramètres à estimer est impossible, les méthodes de Monte-Carlo par chaînes de Markov (MCMC) [Robert and Casella, 1999] permettent théoriquement de fournir un échantillon approximativement...