We consider a Bayesian analysis of linear regression models that can account for skewed error distributions with fat tails. The latter two features are often observed characteristics of empirical data sets, and we will formally incorporate them in the inferential process. A general procedure for...

In this paper we describe the use of modern numerical integration methods for making posterior inferences in composed error stochastic frontier models for panel data or individual cross-sections. Two Monte Carlo methods have been used in practical applications. We survey these two methods in...

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 formulate a general representation of points z E Rn - {O} in terms of pairs (y, r), where r 0, y lies in some space y, and z = ry. In addition, we impose that the representation is unique. An example of such a representation is polar coordinates. As an immediate consequence, we can represent...

We consider the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. We examine the effect of a variety of prior...

This paper considers a Bayesian analysis of the linear regression model under independent sampling from general scale mixtures of Normals. Using a common reference prior, we investigate the validity of Bayesian inference and the existence of posterior moments of the regression and scale...

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...

We model daily catches of fishing boats in the Grand Bank fishing grounds. We use data on catches per species for a number of vessels collected by the European Union in the context of the Northwest Atlantic Fisheries Organization. Many variables can be thought to influence the amount caught: a...

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...