We represent random variables $Z$ that take values in $\Re^n-\{0\}$ as $Z=RY$, where $R$ is a positive random variable and $Y$ takes values in an $(n-1)$-dimensional space $\cal Y$. By fixing the distribution of either $R$ or $Y$, while imposing independence between them, different classes of...

We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian model averaging (BMA). We find that the posterior probability is very spread among many models suggesting the superiority of BMA over choosing any single model. Out-of-sample predictive results...

This paper has two main purposes. Firstly, we develop various ways of defining efficiency in the case of multiple output production. We specifically consider the case where some of the outputs are undesirable, such as pollutants. We investigate how these efficiency definitions relate to one...

In contrast to a posterior analysis given a particular sampling model, posterior model probabilities in the context of model uncertainty are typically rather sensitive to the specification of the prior. In particular, 'diffuse' priors on model-specific parameters can lead to quite unexpected...

The reference prior algorithm (Berger and Bernardo 1992) is applied to multivariate location-scale models with any regular sampling density, where we establish the irrelevance of the usual assumption of Normal sampling if our interest is in either the location or the scale. This result...

This paper deals with the issue of modelling daily catches of fishing boats in the Grand Bank fishing grounds. We have data on catches per species for a number of vessels collected by the European Union (EU) in the context of the Northwest Atlantic Fisheries Organization (NAPO). Many variables...

We present a simple model of management teams where the time it takes to make decisions is related to the size of the committee. We characterize the situations where larger or smaller sizes of the management team are desirable depending on the covariance structure of the signals that the...

In this chapter, we described a Bayesian approach to efficiency analysis using stochastic frontier models. With cross-sectional data and a log-linear frontier, a simple Gibbs sampler can be used to carry out Bayesian inference. In the case of a nonlinear frontier, more complicated posterior...

In this paper we develop Bayesian tools for estimating multi-output production frontiers in applications where only input and output data are available. Firm-specific inefficiency is measured relative to this frontier. Our work has important differences from the existing literature, which either...

This paper deals with the issue of modeling daily catches of fishing boats in the Grand Bank fishing grounds. We have data on catches per species for a number of vessels collected by the European Union (EU) in the context of the Northwest Atlantic Fisheries Organization (NAFO). Many variables...