A time series analysis was performed on medical scheme statistics with the aim of determining current and future demographic and economic trends. This would enable one to reach a better perspective on the current and future financial dilemma presently experienced by medical schemes. Data for the...

GARCH models are commonly used as latent processes in econometrics, financial economics, and macroeconomics. Yet no exact likelihood analysis of these models has been provided so far. In this paper we outline the issues and suggest a Markov chain Monte Carlo algorithm which allows the...

This paper is concerned with the Bayesian estimation and comparison of flexible, high dimensional multivariate time series models with time varying correlations. The model proposed and considered here combines features of the classical factor model with that of the heavy tailed univariate...

In 2003, an industry-financed, government-administered buyback of trawl fishing permits and vessels took place on the US West Coast, resulting in the retirement of about one-third of the limited-entry trawl fleet. The lack of cost data in this fishery precludes an analysis of how the buyback has...

A common feature of ecological datasets is their tendency to contain many zero values. Statistical inference based on such data is likely to be inefficient or wrong unless careful thought is given to how these zeros arose and how best to model them. In this paper, we propose a framework for...

This dissertation quantitatively evaluates selected labor market policies in a search-matching model with skill heterogeneity where high-skilled workers can take temporary jobs with skill requirements below their skill levels. The joint posterior distribution of structural parameters of the...

Advances in capture–recapture methodology have allowed the inclusion of continuous, time-dependent individual-covariates as predictors of survival and capture probabilities. The problem posed by these covariates is that they are only observed for an individual when that individual is captured....

The present dissertation consists of three stand-alone research papers that all deal with factor models from a Bayesian perspective, both in a theoretical and an empirical setup. More precisely, the thesis is organized in a progressive way as follows: Chapter 1 briefly presents the general...

We present an hierarchical Bayes approach to modeling parameter heterogeneity in generalized linear models. The model assumes that there are relevant subpopulations and that within each subpopulation the individual-level regression coefficients have a multivariate normal distribution. However,...

The statistical inference based on the ordinary least squares regression is sub-optimal when the distributions are skewed or when the quantity of interest is the upper or lower tail of the distributions. For example, the changes in Total Sharp Scores (TSS), the primary measurements of the...