Econometric methods for distributional policy effects. This project aims to develop new econometric methods that can measure distributional policy effects by accounting for heterogeneous policy impacts among observationally equivalent individuals. The project expects to develop quantile regression methods under a difference-in-differences framework that accommodates issues of censoring and sample selection. The outcomes of this project are expected to substantially broaden the scope of the stand ....Econometric methods for distributional policy effects. This project aims to develop new econometric methods that can measure distributional policy effects by accounting for heterogeneous policy impacts among observationally equivalent individuals. The project expects to develop quantile regression methods under a difference-in-differences framework that accommodates issues of censoring and sample selection. The outcomes of this project are expected to substantially broaden the scope of the standard mean difference-in-differences approach and have significant contributions to empirical studies in the future. The project intends to provide statistically valid inferential procedures and conduct simulation exercise and empirical studies relevant to policy evaluation for the benefit of Australia and other jurisdictions.Read moreRead less
Macroeconomic and Financial Modelling in an Era of Extremes. This project aims to develop methods to allow workhorse models in economics and finance to better reflect tail events--low probability extreme events, such as the Global Financial Crisis and the COVID-19 pandemic. It intends to address fundamental technical challenges in the estimation of such models, develop a coherent framework for counterfactual analysis of these models and propose methods to apply these models in a big-data environ ....Macroeconomic and Financial Modelling in an Era of Extremes. This project aims to develop methods to allow workhorse models in economics and finance to better reflect tail events--low probability extreme events, such as the Global Financial Crisis and the COVID-19 pandemic. It intends to address fundamental technical challenges in the estimation of such models, develop a coherent framework for counterfactual analysis of these models and propose methods to apply these models in a big-data environment. Expected outcomes include new insights into the transmission of tail risks in the global economic and financial system. This should provide significant benefits, including guidance to Australian and international policymakers charged with maintaining stability in the face of extreme events.Read moreRead less
Loss-based Bayesian Prediction. This project proposes a new paradigm for prediction. Using state-of-the-art computational methods, the project aims to produce accurate, fit for purpose, predictions which, by design, reduce the loss incurred when the prediction is inaccurate. Theoretical validation of the new predictive method, without reliance on knowledge of the correct statistical model, is an expected outcome, as is an extensive numerical assessment of its performance in empirical settings. T ....Loss-based Bayesian Prediction. This project proposes a new paradigm for prediction. Using state-of-the-art computational methods, the project aims to produce accurate, fit for purpose, predictions which, by design, reduce the loss incurred when the prediction is inaccurate. Theoretical validation of the new predictive method, without reliance on knowledge of the correct statistical model, is an expected outcome, as is an extensive numerical assessment of its performance in empirical settings. The new paradigm should produce significant benefits for all fields in which the consequences of predictive inaccuracy are severe. Problems that lead to substantial economic, financial or environmental loss if predictions are incorrect will be given particular attention.Read moreRead less
New methods for modelling complex trends in climate and energy time series. The project aims to contribute to Australian and international efforts on emission control by advancing the methods for quantifying the relationships between energy production, emission and climate, and assessing the real and financial risks associated with changing the ways in which economies produce and use energy. The project is interdisciplinary and expects to develop new knowledge in the areas of energy and climate ....New methods for modelling complex trends in climate and energy time series. The project aims to contribute to Australian and international efforts on emission control by advancing the methods for quantifying the relationships between energy production, emission and climate, and assessing the real and financial risks associated with changing the ways in which economies produce and use energy. The project is interdisciplinary and expects to develop new knowledge in the areas of energy and climate econometrics. The anticipated outcomes of this project are new methods for modelling variables with complex trends, and an innovative data-driven approach for learning from policy experiences of other countries. This should provide significant benefits by enabling evidence-based policy making in the era of climate change. Read moreRead less
Statistical Analysis of State-Dependent Government Spending Multipliers. This project aims to provide a new statistical analysis of the government spending multiplier by acknowledging that government spending is the sum of sectoral spending which has heterogeneous effects on the economy. An added complication is that the multiplier can also be state-dependent, meaning that its magnitude can differ across recessions and expansions. Expected outcomes of this project include a better understanding ....Statistical Analysis of State-Dependent Government Spending Multipliers. This project aims to provide a new statistical analysis of the government spending multiplier by acknowledging that government spending is the sum of sectoral spending which has heterogeneous effects on the economy. An added complication is that the multiplier can also be state-dependent, meaning that its magnitude can differ across recessions and expansions. Expected outcomes of this project include a better understanding of the components of the multiplier by novel decomposition and the development of a new statistical test for the state-dependency of the multiplier. This should provide significant benefits to researchers by bringing in new tools and insights and to policymakers by providing timely guidance on fiscal policies.Read moreRead less
Micro-panel data with non-linear error components. This project aims to develop methods for panel data models with heterogeneous marginal effects and discrete choice outcomes, controlling for unobserved common factors and nonlinear error components; and apply the methodologies to analyse alcohol-fuelled violence and drug-related harm in Australia. The project lies at the forefront of advances in econometrics, and the outcomes are expected to broaden and deepen Australia’s knowledge base. Empiric ....Micro-panel data with non-linear error components. This project aims to develop methods for panel data models with heterogeneous marginal effects and discrete choice outcomes, controlling for unobserved common factors and nonlinear error components; and apply the methodologies to analyse alcohol-fuelled violence and drug-related harm in Australia. The project lies at the forefront of advances in econometrics, and the outcomes are expected to broaden and deepen Australia’s knowledge base. Empirical outcomes should inform and evaluate evidence-based policy interventions for crime prevention, and influence policy making about public transport and economic growth.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100316
Funder
Australian Research Council
Funding Amount
$435,431.00
Summary
Population genomic methods for modelling bacterial pathogen evolution. This project aims to develop novel techniques to model bacterial genome evolution and improve our understanding of how major agricultural and human pathogens, including Enterococcus, Salmonella and E. coli, evolve. The project expects to generate new knowledge about how horizontal gene transfer shapes the evolution of bacteria and how these dynamics vary over different temporal scales. Expected outcomes include methodological ....Population genomic methods for modelling bacterial pathogen evolution. This project aims to develop novel techniques to model bacterial genome evolution and improve our understanding of how major agricultural and human pathogens, including Enterococcus, Salmonella and E. coli, evolve. The project expects to generate new knowledge about how horizontal gene transfer shapes the evolution of bacteria and how these dynamics vary over different temporal scales. Expected outcomes include methodological advances that will enable the analysis of massive contemporary datasets. These methods and resulting analyses will provide significant benefits including informing the design of superior long-term interventions to reduce bacterial disease in both agriculture and health that are robust to the evolution of bacteria.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC220100035
Funder
Australian Research Council
Funding Amount
$4,958,927.00
Summary
ARC Training Centre for Hyphenated Analytical Separation Technologies . The toughest analytical science challenges typically require advanced analytical technologies to acquire the desired solutions. In the field of separation science this inevitably involves hyphenated separation technologies, specifically the combination of chromatography and mass spectrometry. Advancing this technology to its full capability requires the collaborative strength of academic, industry and end-user partnerships, ....ARC Training Centre for Hyphenated Analytical Separation Technologies . The toughest analytical science challenges typically require advanced analytical technologies to acquire the desired solutions. In the field of separation science this inevitably involves hyphenated separation technologies, specifically the combination of chromatography and mass spectrometry. Advancing this technology to its full capability requires the collaborative strength of academic, industry and end-user partnerships, providing the materials and inspiration for young researchers to apply novel hyphenated methods to complex environmental and industrial systems. This Centre will deliver fundamental developments in hyphenated technologies, new analytical capability, and applied outcomes across multiple end-user groups and interests. Read moreRead less
Genetic architecture and evolution of complex traits across populations. Most human traits have a genetic component and display substantial diversity within and among populations. How natural selection changes and maintains genetic variation in human traits is a long-standing question in evolution that the proposed project aims to answer. Using innovative statistical methods and largest genomic “big” datasets ever across populations of different ancestral backgrounds, this project expects to gen ....Genetic architecture and evolution of complex traits across populations. Most human traits have a genetic component and display substantial diversity within and among populations. How natural selection changes and maintains genetic variation in human traits is a long-standing question in evolution that the proposed project aims to answer. Using innovative statistical methods and largest genomic “big” datasets ever across populations of different ancestral backgrounds, this project expects to generate new knowledge on the roles of natural selection in shaping the genetic variation in traits and identify key factors that drive the differentiation of human populations. These outcomes will significantly improve our understanding on the evolution of human traits and adaptation of populations to changing environments.Read moreRead less
Prior sensitivity analysis for Bayesian Markov chain Monte Carlo output. This project aims to develop the first set of techniques to implement an automated output sensitivity analysis for Markov Chain Monte Carlo (MCMC) estimation methods. Computationally intense Bayesian MCMC provide a powerful alternative to classical methods for the estimation of economic models. An obstacle to their wider application is that researchers need to specify prior beliefs about model parameters that will affect t ....Prior sensitivity analysis for Bayesian Markov chain Monte Carlo output. This project aims to develop the first set of techniques to implement an automated output sensitivity analysis for Markov Chain Monte Carlo (MCMC) estimation methods. Computationally intense Bayesian MCMC provide a powerful alternative to classical methods for the estimation of economic models. An obstacle to their wider application is that researchers need to specify prior beliefs about model parameters that will affect the results. The expected outcomes will enable researchers to undertake a routine assessment of the sensitivity of the results to prior inputs.Read moreRead less