Many production processes yield both good outputs and undesirable ones (e.g. pollutants). In this paper, we develop a generalization of a stochastic frontier model which is appropriate for such technologies. We discuss efficiency analysis and, in particular, define technical and environmental...

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...

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 develop Bayesian methods of analysis for a new class of Threshold Autoregressive models: Endogenous Delay Threshold. We apply our methods to the commonly used sunspot data data set and find strong evidence in favor of the EDTAR model over linear and tranditional threshold autoregressions.

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...

We examine the pricing of initial public offering (IPO), seasoned equity offering (SEO) and post-chapter 11 firms using a stochastic frontier methodology. The stochastic frontier framework allows us to model "inefficiency" or the difference between a firm's maximum predicted and its actual...

This paper uses data from 11 countries for 19 years to investigate the forces driving output change in 6 manufacturing sectors. A stochastic production frontier model is adopted which allows for the decomposition of output changes into three types of change: technical, efficiency and input. This...

We examine dynamic asymmetries in US unemployment using non-linear time series models and Bayesian methods. We find strong statistical evidence in favour of a two regime threshold autoregressive model. Empirical results indicate that, once we take into account both parameter and model...

In this paper we begin by developing practical Bayesian methods for inference in a standard equilibrium search model. We then consider two models which are more flexible, but centered over the equilibrium search model using Bayesian priors. Such priors can be used to control the degree of...