Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by...

Time series subject to parameter shifts of random magnitude and timing are commonly modeled with a change-point approach using Chib's (1998) algorithm to draw the break dates. We outline some advantages of an alternative approach in which breaks come through mixture distributions in state...

We model a regression density nonparametrically so that at each value of the covariates the density is a mixture of normals with the means, variances and mixture probabilities of the com- ponents changing smoothly as a function of the covariates. The model extends existing models in two...

A Gaussian copula regression model gives a tractable way of handling a multivariate regression when some of the marginal distributions are non-Gaussian. Our paper presents a general Bayesian approach for estimating a Gaussian copula model that can handle any combination of discrete and...

In this Heckscher-Ohlin-Samuelson model, production of a pollutive good damages production of another good within the same country. Unilateral and bilateral shifts from laissez faire to Pigouvian policy are numerically simulated for cases of low emissions and a high volume of trade, high...

When the disutility of global pollution is measured in units of a numeraire good that is polluting in consumption, the marginal rates of substitution in consumption are generally weighted differently in each country’s calculation of marginal damages. In that case, it is inefficient to...