Problems of identification and inference for 'non-standard' models in complex systems with special reference to finance and teletraffic. The project is concerned with 'non-standard' models needed to deal with complex systems, such as those exhibiting scaling and fractal properties. There is a focus on methods for dealing with heavy tailed distributions and long range dependent observations, for which most standard statistical methods break down, and on applications in finance and telecommunicati ....Problems of identification and inference for 'non-standard' models in complex systems with special reference to finance and teletraffic. The project is concerned with 'non-standard' models needed to deal with complex systems, such as those exhibiting scaling and fractal properties. There is a focus on methods for dealing with heavy tailed distributions and long range dependent observations, for which most standard statistical methods break down, and on applications in finance and telecommunications. An important part of the project concerns model validation for Heyde's fractal activity time geometric Brownian motion model, a candidate minimal description risky asset model to replace the geometric Brownian motion paradigm.Read moreRead less
Inference in partially non-stationary time series models. Economic theories typically specify the long-run relationship between economic variables. However, researchers usually examine the long-run features of the data by fitting a restrictive class of models using criteria that have only proven useful for short-term forecasting. In this project we consider alternative models and modelling strategies that are appropriate for the study of the long-run. We also develop computer intensive (bootstra ....Inference in partially non-stationary time series models. Economic theories typically specify the long-run relationship between economic variables. However, researchers usually examine the long-run features of the data by fitting a restrictive class of models using criteria that have only proven useful for short-term forecasting. In this project we consider alternative models and modelling strategies that are appropriate for the study of the long-run. We also develop computer intensive (bootstrap) methods, which will provide a much-needed improvement over the existing (asymptotic) methods for making inference about the long-run. Our research will lead to more reliable models for long-term planning in business, industry and government.Read moreRead less
Vector ARMA Models and Macroeconomic Modelling: Some New Methodology and Algorithms. Economic variables are strongly related to each other, as well as being strongly related to their recent history. As a result, good dynamic multivariate models are crucial for effective policy making and forecasting in areas of vital national importance such as monetary and fiscal policy, environmental policy and tourism. Our project advances the frontiers of knowledge in multivariate time series modelling. The ....Vector ARMA Models and Macroeconomic Modelling: Some New Methodology and Algorithms. Economic variables are strongly related to each other, as well as being strongly related to their recent history. As a result, good dynamic multivariate models are crucial for effective policy making and forecasting in areas of vital national importance such as monetary and fiscal policy, environmental policy and tourism. Our project advances the frontiers of knowledge in multivariate time series modelling. The outcome of this project will be immediately useful for macroeconomic policy makers such as the Reserve Bank of Australia and the Treasury, and for industry bodies such as Tourism Australia. Read moreRead less