Semi-parametric bootstrap-based inference in long-memory models. Given the long lead times involved in implementing economic decisions, a clear understanding of the long-term dynamics driving key variables is crucial. This project will produce significant advances in the analysis of long-range dependence, with decisions underpinned by more accurate and robust statistical information as a consequence.
Approximate Bayesian computation in state space models. Economic and financial data frequently exhibit dynamic patterns, driven by unobserved processes that relate to the behaviour of economic agents, or to institutional and technological change. To gain insight into such 'latent' processes is of paramount importance in terms of both understanding the economy and producing accurate, readily up-dated, forecasts of its future performance. Using a Bayesian approach, new simulation-based statistical ....Approximate Bayesian computation in state space models. Economic and financial data frequently exhibit dynamic patterns, driven by unobserved processes that relate to the behaviour of economic agents, or to institutional and technological change. To gain insight into such 'latent' processes is of paramount importance in terms of both understanding the economy and producing accurate, readily up-dated, forecasts of its future performance. Using a Bayesian approach, new simulation-based statistical methods for analysing latent variable models are proposed. Emphasis is given to the development of relatively simple techniques that are applicable to a wide range of empirically relevant models, with a view to improving the access of non-specialists to this powerful form of statistical analysis.Read moreRead less
The validation of approximate Bayesian computation. This project aims to establish the theoretical validity of approximate Bayesian computation (ABC) and to develop diagnostic methods for assessing its reliability in empirical applications. Given the increased complexity of modern statistical models, new ways of conducting statistical inference are needed. Approximate Bayesian computation is a new statistical tool. This project expects its findings will be useful in all fields where complex phen ....The validation of approximate Bayesian computation. This project aims to establish the theoretical validity of approximate Bayesian computation (ABC) and to develop diagnostic methods for assessing its reliability in empirical applications. Given the increased complexity of modern statistical models, new ways of conducting statistical inference are needed. Approximate Bayesian computation is a new statistical tool. This project expects its findings will be useful in all fields where complex phenomena feature and approximate methods are the only feasible way of understanding those phenomena.Read moreRead less