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 interpretable...

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...

BC-PROD is a prototype modelling and optimization system designed and able to tackle a wide variety of the discrete-time lot-sizing problems arising both in the practice and in literature. To use BC-PROD, the user needs to formulate his/her problem as a mixed integer program using XPRESS-MP's...

Behavioral economics has shaken the view that individuals have well-defined, consistent and stable preferences. This raises a challenge for welfare economics, which takes as a key postulate that individual preferences should be respected. We agree with Bernheim (2009) and Bernheim and Rangel...

In this paper we examine whether, and how, welfare economics should incorporate the insights from happiness and satisfaction studies. Our main point is that measuring well-being by reported satisfaction levels can come in conáict with individuals judgments about their own lives and that these...

An important class of structural econometric models (nonlinear rational expectations,option pricing, auction models,...) characterize observable variables as highly nonlinear transformations of some latent variables. These transformations are one-to-one but they depend on the unknown...

In this paper, I first prove an integral representation theorem: Every quasi-integral on a Stone lattice can be represented by a unique upper-continuous capacity. I then apply this representation theorem to study the topological structure of the space of all upper-continuous capacities on a...

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...