Most hypotheses in binary response models are composite. The null hypothesis is usually that one or more slope coefficients are zero. Typically, the sequence of alternatives of interest is one in which the slope coefficients are increasing in absolute value. In this papar, we prove that the...

This paper considers the case of Bayesian learning about the relationship between the greenhouse-gas level and temperature rise. Learning takes time because of a stochastic shock to the realized global mean temperature. The paper illustrates the difficulty of quickly learning about the...

The notion of disapprobation is defined. It is designed to capture some features of misspecification in a decision-theoretic framework. Moreover, it is a sample-based notion so it is well-suited for the study of misspecification in Bayesian contexts. Some elementary examples of disapprobation...

A unit root test is usually carried out by using the regression test introduced by Dickey and Fuller (1979). Under the null hypothesis the series should be a random walk. But a non-stationary series can usually be decomposed into a random walk and a stationary component. This is what is done in...

We propose in this paper a Bayesian approach with a noninformative prior distribution developed in Mengersen and Robert (1996) and Robert and Titterington (1996) in the setup of mixtures of distributions and hidden Markov models, respectively.

This paper explains how the Gibbs sampler can be used to perform Bayesian inference on GARCH models. Although the Gibbs sampler is usually based on the analytical knowledge of the full conditional posterior densities, such knowledge is not available in regression models with GARCH errors. We...

This paper explains how the Gibbs sampler can be used to perform Bayesian inference on GARCH models. Although the Gibbs sampler is usually based on the analytical knowledge of the full conditional posterior densities, such knowledge is not available in regression models with GARCH errors. We...