This thesis discusses issues arising in the analysis of repeated measurement studies with missing data. The first part of the thesis is motivated by a study where continuous and bounded longitudinal data form the outcome of interest. The aim of this study is to investigate the change over time...

We examine the issue of variable selection in linear regression modeling, where we have a potentially large amount of possible covariates and economic theory offers insufficient guidance on how to select the ap- propriate subset. Bayesian Model Averaging presents a formal Bayesian solution to...

In longitudinal and multivariate settings incomplete data, due to missed visits, dropouts or non-return of questionnaires are quite common. A longitudinal trial in which potentially informative missingness occurs is the Collaborative Ankle Support Trial (CAST). The aim of this study is to...

This thesis is concerned with Bayesian forecasting and sequential estimation. The concept of multiple discounting is introduced in order to achieve parametric and conceptual parsimony. In addition, this overcomes many of the drawbacks of the Normal Dynamic Linear Model (DLM) specification which...

This thesis concerns theoretical and practical Bayesian modelling of multivariate time series. Our main goal is to intruduce useful, flexible and tractable multivariate forecasting models and provide the necessary theory for their practical implementation. After a brief review of the dynamic...

The purpose of this paper is to describe the performance of generalized empirical likelihood (GEL) methods for time series instrumental variable models specified by nonlinear moment restrictions as in Stock and Wright (2000, Econometrica 68, 1055?1096) when identification may be weak. The paper...

This paper establishes the generic size and structure of the second-best Pareto frontier and its various components in private ownership economies with Ramsey taxation. It provides conditions under which the second-best Pareto frontier of an economy with H consumers will have the expected...

This thesis develops three new classes of Bayesian graphical models to forecast multivariate time series. Although these models were originally motivated by the need for flexible and tractable forecasting models appropriate for modelling competitive business markets, they are of theoretical...

The aim of this thesis is to generalise Bayesian Forecasting processes to models where normality assumptions are, not appropriate. In particular I develop models that can change their minds and I utilise Catastrophe Theory in their description. Under squared-error loss types of criteria the...

Multifractal processes have recently been proposed as a new formalism for modelling the time series of returns in finance. The major attraction of these processes is their ability to generate various degrees of long memory in different powers of returns - a feature that has been found in...