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HIGH-DIMENSIONAL DYNAMIC SYSTEMS

The network collaborates with international researchers on the analysis and application of complex, dynamic and flexible models for high-dimensional statistical data. We aim to create new econometric and statistical methods that exploit the power of computing and information in large data sets. These will be used to inform policy decisions on important global issues such as climate change, energy demand, population ageing and the stability of financial systems.

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About us

Climate change is one of the greatest challenges of our time. Empirical evidence shows that global greenhouse gases affect global temperatures and this has resulted in international efforts to reduce emissions. The achievement of these emission goals in the most economically efficient way over time requires measuring responses to economic changes (such as price increases or changes in production inputs)  that are often broadly referred to as “elasticities”. To estimate the elasticities from observational data is challenging because of the complex trends in emission and climate data, the nonlinearities that occur in the ever changing modes of energy production and the role that risk plays in the energy markets.

Another political concern across the globe is the waiting times for elective surgeries. In a public-financed medical system, medical demand easily exceeds supply. Some patients wait for several months, sometimes years, before they are admitted to hospitals. Two of the key questions in this area are whether waiting times are determined not only by medical need but by other non-clinical factors and also whether there are systematic differences across hospitals. The good handling of waiting times is crucial to governments.

The curse of dimensionality

Addressing issues like climate change elasticity and modelling waiting times for planned surgery requires an understanding of complex dynamic relationships within high-dimensional sets of complex data. A major obstacle in modelling this sort of data is that the analysts’ ability to focus on particular aspects of interest is impeded by the sheer number of potentially relevant data series that might be taken into account. This is known as the curse of dimensionality. Further, the complicated ways in which large groups of variables in systems such as climatic and financial systems change over time are difficult to estimate and explain. The International Network on High-Dimensional Dynamic Systems houses leading authorities on modelling such trends.  The analysis of large and trending systems of data brings with it many practical challenges: how to interactively plot and visualize the data and assess the significance of structures discovered by exploratory methods; how to develop and estimate models that incorporate rich relationships and use them to produce accurate and informative predictions.

Collaborating with renowned scholars

To address these critical issues, the Department of Econometrics and Business Statistics at the Monash Business School has established an International Network on High-Dimensional Dynamic Systems.  The Network links Monash researchers with a select group of internationally renowned econometricians and statisticians from Cambridge, Columbia, National Tsinghua in Taiwan, Paris, Warwick and Yale Universities, to collaborate on the analysis and application of complex, dynamic and flexible models for high-dimensional statistical data. The establishment of this Network further facilitates and enhances collaborative research activities within the Monash Business School with some of the best and leading centres around the world, such as the Cowles Foundation for Research in Economics at Yale University, Climate Econometrics at the University of Oxford, and the Centre for Research in Econometric Analysis of Time Series at Aarhus University.

The main aims of the Network on High-Dimensional Dynamic Systems are to tackle new and challenging issues in modern econometrics and statistics and to use the recognized expertise to establish new research programs in big data analytics. Finally, to offer solutions and policies to solve compelling problems in a wide range of areas, such as the environment, health care, population ageing, insurance, and the stability of financial systems.

Professor Jiti Gao, Professor Gael Martin, Professor Farshid Vahid, Professor Dianne Cook, Professor Rob J. Hyndman and Professor Heather Anderson.

Our researchers

Heather Anderson is the Maureen Brunt Professor of Economics and Econometrics. She has been the Head of Department of Econometrics and Business Statistics at Monash Business School since 2016. She is an Elected Fellow of the Academy of Social Sciences in Australia and an Elected Fellow of the International Association of Applied Econometrics. Her research interests include econometrics, nonlinear time series analysis, empirical finance and macro econometrics.

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Dianne Cook is Professor of Business Analytics in the Department of Econometrics and Business Statistics at Monash Business School. She is a Fellow of the American Statistical Association. Her research focuses on data science, data visualisation, exploratory data analysis, data mining, high-dimensional methods and statistical computing.

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Jiti Gao is the Donald Cochrane Chair of Business and Economics at Monash Business School. He is an Elected Fellow of the Academy of Social Sciences in Australia, and a Founding Member of the Society for Financial Econometrics and an Elected Member of the International Statistical Institute. He was an Australian Professorial Fellow from 2010-2014. His research areas are econometric theory, nonparametric econometrics, nonlinear time series and panel data analysis.

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Rob J. Hyndman is Professor of Statistics in the Department of Econometrics and Business Statistics at Monash Business School. He is the incoming Head of the Department of Econometrics and Business Statistics from 2019. He is also a Director of the International Institute of Forecasters, an elected member of the International Statistical Institute, and an accredited statistician with the Statistical Society of Australia. His research interests are forecasting, time series analysis, computational statistics, and exploratory data analysis.

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Gael Martin is Professor of Econometrics in the Department of Econometrics and Business Statistics at Monash Business School and a past Australian Research Council Future Fellow. Her research focuses mainly on Bayesian inference, computational statistics, probabilistic forecasting, financial econometrics and time series analysis.

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Farshid Vahid is Professor in the Department of Econometrics and Business Statistics at Monash Business School. He is also an elected fellow of the Academy of Social Sciences in Australia and an elected member of the International Association of Applied Econometrics. His research interests include econometrics, applied economics, time series analysis, and econometric analysis of experimental data.

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Our international partners

Oliver Linton is Professor of Political Economy and Director of Research at the University of Cambridge. He is a Fellow of the British Academy, a Fellow of both the Econometric Society and the Institute of Mathematical Statistics. His research focuses on econometric theory and empirical finance. Serena Ng is Professor of Economics at the Data Science Institute at Columbia University. She is a Fellow of the Econometric Society, a member of the Centre for Financial and Business Analytics and a member of the working group on Computational Social Science. Her research interests include macroeconomics, time series and econometrics.
Peter Phillips is the Sterling Professor of Economics and Statistics at Yale University. He is a Fellow of the Econometric Society, the Institute of Mathematical Statistics, the American Statistical Association and the American Academy of Arts and Sciences. Among his research interests are econometric theory, financial econometrics, time series analysis, panel data, spatial econometrics, micro econometrics, applied macroeconomics and climatological trends. Eric Renault is the C.V. Starr Professor of Commerce, Organizations and Entrepreneurship, Professor of Economics at Brown University. He is a Fellow of the Econometric Society and the Journal of Econometrics and past President of the Society for Financial Econometrics. His research interests include econometric theory, financial econometrics and simulation-based inference.
Christian Robert is Professor of Statistics at the Universite Paris Dauphine and (fractional) Professor of Statistics at the University of Warwick. He is a Fellow of the Royal Statistical Society, the Institute of Mathematical Statistics, and the American Statistical Association, as well as being a Medallion Lecturer of the IMS. His research covers all aspects of the field of Bayesian statistics, from decision theory, prior specification and model selection, to the theory and implementation of Bayesian computational methods. Galit Shmueli is Tsing Hua Distinguished Professor of Business Analytics at the Institute of Service Science, and Director of the Centre for Service Innovation and Analytics at the College of Technology Management, National Tsing Hua University. Her research focuses on statistical and data mining methodology with applications in information systems and healthcare.

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