A message coming out of the recent Bayesian literature on cointegration is that it is important to elicit a prior on …

This paper is concerned with the Bayesian estimation and comparison of flexible, high dimensional multivariate time …

The present dissertation consists of three stand-alone research papers that all deal with factor models from a Bayesian … that can be implemented to conduct Bayesian inference in this kind of model. Chapters 2 and 3 introduce some important …

Bayesian approach: information in longer time series is aggregated into a prior which is then used in the estimation of … from the Georgian economy and find that resulting (Bayesian) VAR models on average perform 7% better than standard …

Bayesian approach: information in longer time series is aggregated into a prior which is then used in the estimation of … from the Georgian economy and find that resulting (Bayesian) VAR models on average perform 7% better than standard …

via Monte Carlo integration. In the application to time series models, we propose a Bayesian approach to the robust …

sample theoretical justifications of the semiparametric AFT model. Chapter 3 proposes a semiparametric Bayesian estimation …

The main objective of this PhD was to further develop Bayesian spatio-temporal models (specifically the Conditional …

modelling to integrated Bayesian modelling using MCMC, via bootstrapping and stochastic simulation. It also covers a broad range … harvest rate. Our approach uses individual-based modeling and Bayesian analysis to investigate the effect of shadows on … statistical model, and estimates parameters using both maximum likelihood and Bayesian MCMC.The sixth chapter (Hoyle 2002, peer …