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...

We propose a new model of chartist-fundamentalist-interaction in which both groups of traders are allowed to select endogenously between different forecasting models and different investment horizons. Stochastic interest rates in both countries and different behavioral assumptions for...

We present a new, full multivariate framework for modelling the evolution of conditional correlation between financial asset returns. Our approach assumes that a vector of asset returns is shocked by a vector innovation process the covariance matrix of which is timedependent. We then employ an...

A general methodology for time series modelling is developed which works down from distributional properties to implied structural models including the standard regression relationship. This general to specific approach is important since it can avoid spurious assumptions such as linearity in...

Background: Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling techniques to...

This paper shows that coordination failure and contractual incompleteness can lead to socially excessive investment. Firms and workers choose investment levels then enter a stochastic matching process. If investment levels are discrete, then if match frictions are low enough, high investing...