Large dynamic time-varying models for structural macroeconomic inference. This project aims to broaden the range of macroeconomic models that have an integrated capacity for both greater realism and efficiency in analysis. This approach will be applied to two contexts at the forefront of current macroeconomic research, the effects of noisy productivity signals on business cycles and the effects of fiscal policy shocks. Flexible macro-econometric models underpin accurate inference by economists ....Large dynamic time-varying models for structural macroeconomic inference. This project aims to broaden the range of macroeconomic models that have an integrated capacity for both greater realism and efficiency in analysis. This approach will be applied to two contexts at the forefront of current macroeconomic research, the effects of noisy productivity signals on business cycles and the effects of fiscal policy shocks. Flexible macro-econometric models underpin accurate inference by economists and policymakers and the project outputs should provide widespread and significant benefits by improving policy and boosting Australia’s comparative advantage.Read moreRead less
Closing the Gap Between Theory and Data in Macroeconometrics. This project aims to bring econometric models (the empirical vehicle for inference) and economic models (the theory) closer together. A new model is intended to be proposed that will address a significant issue with the interpretation of the outputs of the econometric models. As a first contribution, the project is expected to develop the model and an inferential framework for this model using probability theory on manifolds. In a sec ....Closing the Gap Between Theory and Data in Macroeconometrics. This project aims to bring econometric models (the empirical vehicle for inference) and economic models (the theory) closer together. A new model is intended to be proposed that will address a significant issue with the interpretation of the outputs of the econometric models. As a first contribution, the project is expected to develop the model and an inferential framework for this model using probability theory on manifolds. In a second contribution, it is expected to construct an algorithm to permit inference leading to outputs useful to policy analysts. The model is intended to be parsimonious, which facilitates the development of a time-varying version to allow the model to evolve with the economy and provide better policy guidance.Read moreRead less
Measuring the Commercial Real Estate Sector in Australia. This project aims to address a significant gap in our understanding of the Australian commercial real estate sector. It will use detailed data to develop sophisticated models of the prices of commercial buildings. Expected outcomes include a suite of commercial real estate price indexes for Australia, by region and property type, and a comprehensive and transparent examination of the methods used to construct them. This will shed light on ....Measuring the Commercial Real Estate Sector in Australia. This project aims to address a significant gap in our understanding of the Australian commercial real estate sector. It will use detailed data to develop sophisticated models of the prices of commercial buildings. Expected outcomes include a suite of commercial real estate price indexes for Australia, by region and property type, and a comprehensive and transparent examination of the methods used to construct them. This will shed light on a hitherto poorly measured sector and provide significant benefits by better informing market participants, guiding statistical agencies in developing such measures and better-enabling policymakers, banks, superfunds and macroprudential authorities to understand the risk profile of the sector.Read moreRead less
Understanding macroeconomic fluctuations with unobserved networks. Whilst empirical evidence suggests that firm-level shocks can have large aggregate effects, via network connections, macroeconomic policies have mostly an aggregate nature. This project aims to build a new framework to disentangle aggregate shocks from shocks to individual units. The major innovations are i) to infer the network from the data and ii) to jointly estimate aggregate factors and network effects. Expected outcomes are ....Understanding macroeconomic fluctuations with unobserved networks. Whilst empirical evidence suggests that firm-level shocks can have large aggregate effects, via network connections, macroeconomic policies have mostly an aggregate nature. This project aims to build a new framework to disentangle aggregate shocks from shocks to individual units. The major innovations are i) to infer the network from the data and ii) to jointly estimate aggregate factors and network effects. Expected outcomes are i) measures of systemic risk and ii) a theoretical framework to study the optimality of aggregate versus sectoral stabilization policies. Benefits include a better understanding of macroeconomic fluctuations in Australia and proposed economic policies to mitigate large and persistent declines in employment and GDP.Read moreRead less
Reliability of purchasing power parities from the World Bank. This project aims to provide an econometric framework to estimate purchasing power parities (PPPs) and a method to compute standard errors associated with the World Bank’s International Comparison Programme (ICP)’s PPPs. The ICP regularly compiles and publishes estimates of PPPs of currencies and real incomes. These results are used for study of global inequality and poverty; macroeconomic analysis; the Human Development Index; and cr ....Reliability of purchasing power parities from the World Bank. This project aims to provide an econometric framework to estimate purchasing power parities (PPPs) and a method to compute standard errors associated with the World Bank’s International Comparison Programme (ICP)’s PPPs. The ICP regularly compiles and publishes estimates of PPPs of currencies and real incomes. These results are used for study of global inequality and poverty; macroeconomic analysis; the Human Development Index; and cross-country productivity comparisons. However, no estimates of ICP PPPs’ reliability are available. Results from this project are likely to improve the quality of widely used data sets including the Penn World Tables and the University of Queensland International Comparison Database relevant to banking.Read moreRead less
Advances in Sequential Monte Carlo Methods for Complex Bayesian Models. This project aims to develop efficient statistical algorithms for parameter estimation of complex stochastic models that currently cannot be handled. Parameter estimation is an essential component of mathematical modelling for answering scientific questions and revealing new insights. Current parameter estimation methods can be inefficient and require too much user intervention. This project will develop novel Bayesian alg ....Advances in Sequential Monte Carlo Methods for Complex Bayesian Models. This project aims to develop efficient statistical algorithms for parameter estimation of complex stochastic models that currently cannot be handled. Parameter estimation is an essential component of mathematical modelling for answering scientific questions and revealing new insights. Current parameter estimation methods can be inefficient and require too much user intervention. This project will develop novel Bayesian algorithms that are optimally automated and efficient by exploiting ever-improving parallel computing devices. The new methods will allow practitioners to process realistic models, enabling new scientific discoveries in a wide range of disciplines such as biology, ecology, agriculture, hydrology and finance.Read moreRead less
A Novel Approach to Semi-Supervised Statistical Machine Learning. Recent successes in the construction of classifiers for making diagnoses and predictions are due in part to their using much data labelled with respect to their class of origin. But typically there are little labelled data but plentiful unlabelled data. The goal of semi-supervised learning (SSL) is to leverage large amounts of unlabelled data to improve the performance using only small labelled datasets and so SSL is of paramount ....A Novel Approach to Semi-Supervised Statistical Machine Learning. Recent successes in the construction of classifiers for making diagnoses and predictions are due in part to their using much data labelled with respect to their class of origin. But typically there are little labelled data but plentiful unlabelled data. The goal of semi-supervised learning (SSL) is to leverage large amounts of unlabelled data to improve the performance using only small labelled datasets and so SSL is of paramount importance to applications where it is expensive or impractical to obtain much labelled data. The project is to develop a novel SSL approach that adopts a missingness mechanism for the missing labels to build a classifier that not only improves accuracy but it can be greater than if the missing labels were known.
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Stochastic majorization--minimization algorithms for data science. The changing nature of acquisition and storage data has made the process of drawing inference infeasible with traditional statistical and machine learning methods. Modern data are often acquired in real time, in an incremental nature, and are often available in too large a volume to process on conventional machinery. The project proposes to study the family of stochastic majorisation-minimisation algorithms for computation of inf ....Stochastic majorization--minimization algorithms for data science. The changing nature of acquisition and storage data has made the process of drawing inference infeasible with traditional statistical and machine learning methods. Modern data are often acquired in real time, in an incremental nature, and are often available in too large a volume to process on conventional machinery. The project proposes to study the family of stochastic majorisation-minimisation algorithms for computation of inferential quantities in an incremental manner. The proposed stochastic algorithms encompass and extend upon a wide variety of current algorithmic frameworks for fitting statistical and machine learning models, and can be used to produce feasible and practical algorithms for complex models, both current and future.
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Building A Better Built Environment for Older Australian's Ageing-in-place. Most older Australians prefer to age in place after their retirement. This project aims to understand how the built environment as a comprehensive system supports (or hinders) their ageing-in-place given that the existing Australian built environment fails to meet older Australians' requirements for independent living. This project expects to generate new knowledge in the area of ageing-friendly communities using Bayesia ....Building A Better Built Environment for Older Australian's Ageing-in-place. Most older Australians prefer to age in place after their retirement. This project aims to understand how the built environment as a comprehensive system supports (or hinders) their ageing-in-place given that the existing Australian built environment fails to meet older Australians' requirements for independent living. This project expects to generate new knowledge in the area of ageing-friendly communities using Bayesian Network analysis and interactive design charrettes. Expected outcomes include an evidence-based Bayesian network model that determines how the built environment affects independent living in the community and design innovation and guidelines to improve the built environment design for older Australians' ageing-in-place.Read moreRead less
Identification of causal variants for complex traits. The aim of this project is to identify causal variants for complex traits in cattle and humans. Although most important traits in agriculture, medicine and evolution are complex traits, very few of the genetic variants affecting these traits are known and this undermines our understanding of how genetic variants affect a trait and practical uses of this knowledge. Huge datasets of individuals with genome sequence and phenotypes and new statis ....Identification of causal variants for complex traits. The aim of this project is to identify causal variants for complex traits in cattle and humans. Although most important traits in agriculture, medicine and evolution are complex traits, very few of the genetic variants affecting these traits are known and this undermines our understanding of how genetic variants affect a trait and practical uses of this knowledge. Huge datasets of individuals with genome sequence and phenotypes and new statistical methods provide the opportunity to close this gap. The outcome will be identification of many genomic variants causing variation in complex traits. This will benefit scientific understanding of complex traits and the ability to predict traits for individuals from their genome sequence.Read moreRead less