This paper surveys Bayesian procedures of testing along with different attitudes of econometricians facing testing problems with some sympathy for Bayesian ideas. In the first part, the general Bayesian testing procedures are structured along two main axes. The first one presents the usual...

This work has been prepared as chapter 19 for the volume Econometrics of Panel Data (2nd ed., Matyas and Sevestre eds., Kluwer Academic Publishers). The main emphasis is on modelling whereas inference problems are only sketched. After a short review on the basic issues on marginal models (i.e....

This paper develops new statistical and computational methods for the automatic detection of spatial clusters displaying an over- or under- relative specialization spatial pattern. A probability model provides a space partition into clusters representing homogenous portions of space as far as...

Although neural networks are commonly encountered to solve classification problems, ranking data present specificities which require adapting the model. Based on a latent utility function defined on the characteristics of the objects to be ranked, the approach suggested in this paper leads to a...

One way the social scientists explain phenomena is by building structural models. These models are explanatory insofar as they manage to perform a recursive decomposition on an initial multivariate probability distribution, which can be interpreted as a mechanism. The social scientists should...

It is widely agreed that, in establishing whether variable X causes variable Y, a third variable Z may confound the relation and thus hinder causal assessment. The solution developed within the ‘traditional’ framework is to control for any third variable, susceptible of confounding the...

We first analyse the general problem of admissible conditioning and next consider the evaluation of the loss of information when a non-admissible conditioning is used as an approximaton of the exact posterior distribution. Considering the case of Fisher test, we evaluate from a Bayesian point...

This note argues that a Bayesian framework is almost inescapable when specifying statistical models of the LISREL type, i.e. models involving not only latent and manifest variables but also incidental parameters. Indeed, a careful specification, making every hypothesis explicit and...

In this paper, minimal conditions under which a semi-parametric binary response model is identified in a Bayesian framework are presented and compared to the conditions usually required in a sampling theory framework.

Five different identification problems in mixture models are made explicit. Necessary and sufficient relationships among these problems of identification are analyzed using the concepts of weak and strong identification. This analysis is first particularized under a normality assumption and then...