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Field of Research : Econometric and Statistical Methods
Australian State/Territory : ACT
Australian State/Territory : NSW
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  • Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE150100795

    Funder
    Australian Research Council
    Funding Amount
    $365,000.00
    Summary
    New approaches to estimating nonlinear time-varying macroeconometric models. Quantitative models are essential for formulating good policies. In a changing world, the analysis should be based on models that allow the behaviour of the economy to change over time. Due to computational limitations, however, one is often restricted to linear models, even when nonlinear ones are more appropriate. This project aims to develop new methods for estimating time-varying nonlinear models. Two important appl .... New approaches to estimating nonlinear time-varying macroeconometric models. Quantitative models are essential for formulating good policies. In a changing world, the analysis should be based on models that allow the behaviour of the economy to change over time. Due to computational limitations, however, one is often restricted to linear models, even when nonlinear ones are more appropriate. This project aims to develop new methods for estimating time-varying nonlinear models. Two important applications are also considered: one investigates how the zero lower bound on interest rates affects the monetary policy transmission mechanism; and, the other examines how uncertainties about monetary and fiscal policy affect economic growth and inflation. This project will have strong practical significance for conducting macroeconomic policy.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP150104595

    Funder
    Australian Research Council
    Funding Amount
    $426,700.00
    Summary
    Uncertainty, Risk and Related Concepts in Machine Learning. Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, .... Uncertainty, Risk and Related Concepts in Machine Learning. Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, statistics and different ways of aggregating information. By building a more complete theoretical map it is expected that new machine learning methods will be developed, but more importantly that machine learning will be able to be better integrated into larger socio-technical systems.
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    Funded Activity

    Discovery Projects - Grant ID: DP170103223

    Funder
    Australian Research Council
    Funding Amount
    $320,000.00
    Summary
    Natural resources and ecosystem services in productivity measurement. This project aims to understand sources of productivity growth through addressing theoretical and practical problems in the economics of natural resources and ecosystem services. It will study the valuation of non-renewable resources and ecosystem services, acknowledging their contributions to economic activity and the effect on national income from their depletion and degradation. It will develop approaches to incorporating n .... Natural resources and ecosystem services in productivity measurement. This project aims to understand sources of productivity growth through addressing theoretical and practical problems in the economics of natural resources and ecosystem services. It will study the valuation of non-renewable resources and ecosystem services, acknowledging their contributions to economic activity and the effect on national income from their depletion and degradation. It will develop approaches to incorporating natural resource depletion and degradation into productivity analysis with the aim of better informing environmental, innovation and industry policy.
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    Funded Activity

    Discovery Projects - Grant ID: DP170101283

    Funder
    Australian Research Council
    Funding Amount
    $283,000.00
    Summary
    Measuring inflation expectations and inflation expectations uncertainty. This project aims to construct model-based measures of inflation expectations and inflation expectations uncertainty. Inflation expectations can determine economic outcomes. This project will develop non-linear time-varying models to combine information from noisy and possibly biased measures of inflation expectations from surveys and financial markets. These model-based measures are expected to be better calibrated and to .... Measuring inflation expectations and inflation expectations uncertainty. This project aims to construct model-based measures of inflation expectations and inflation expectations uncertainty. Inflation expectations can determine economic outcomes. This project will develop non-linear time-varying models to combine information from noisy and possibly biased measures of inflation expectations from surveys and financial markets. These model-based measures are expected to be better calibrated and to provide valuable information for policymakers for formulating macroeconomic policies. They can be used to better assess the credibility of monetary policy and shed light on the causes of low inflation rate in developed economies.
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