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Socio-Economic Objective : Expanding Knowledge in Economics
Australian State/Territory : NSW
Research Topic : rapid methods
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  • Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE170100787

    Funder
    Australian Research Council
    Funding Amount
    $331,000.00
    Summary
    Misspecification in models of economic behaviour. This project aims to develop a robust method for estimation and inference with misspecified economic models. Economic models are designed to test hypotheses about economic behaviour and to estimate key parameters, but their validity and accuracy critically depend on the assumption that the model is correctly specified, which is often doubtful. This project will reparametrize the model to allow for misspecification. The project aims to help modell .... Misspecification in models of economic behaviour. This project aims to develop a robust method for estimation and inference with misspecified economic models. Economic models are designed to test hypotheses about economic behaviour and to estimate key parameters, but their validity and accuracy critically depend on the assumption that the model is correctly specified, which is often doubtful. This project will reparametrize the model to allow for misspecification. The project aims to help modellers produce results that better inform decision-makers and help them make more reliable decisions.
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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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    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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    Funded Activity

    Discovery Projects - Grant ID: DP170100987

    Funder
    Australian Research Council
    Funding Amount
    $200,000.00
    Summary
    High-dimensional models with a change point. This project aims to provide a set of estimation and inference procedures for high dimensional quantile regression. Statistical models of threshold regression with change or tipping points are used to explore social issues, including changes in oil and gas prices, effective dosage of drugs and the racial mix in neighbourhoods. To date, using low numbers of variables, the findings have been limited. Big data makes it possible and desirable to solve mor .... High-dimensional models with a change point. This project aims to provide a set of estimation and inference procedures for high dimensional quantile regression. Statistical models of threshold regression with change or tipping points are used to explore social issues, including changes in oil and gas prices, effective dosage of drugs and the racial mix in neighbourhoods. To date, using low numbers of variables, the findings have been limited. Big data makes it possible and desirable to solve more detailed models to provide more accurate results. The quality and accuracy of the project’s results are expected to help governments devise well informed and appropriate policies for social issues.
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    Funded Activity

    Discovery Projects - Grant ID: DP130102408

    Funder
    Australian Research Council
    Funding Amount
    $180,000.00
    Summary
    Asymptotics in non-linear cointegrating regression: theory and applications. This project provides fundamental research in statistics, econometrics and probability. The results on martingales and nonlinear functionals of integrated stochastic processes will apply to a range of statistical, empirical finance and economic models.
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    Funded Activity

    Discovery Projects - Grant ID: DP170104385

    Funder
    Australian Research Council
    Funding Amount
    $288,471.00
    Summary
    Non-linear cointegrating regression with endogeneity. This project aims to develop the asymptotic theory of estimation and statistical inference in models concerned with non-linear co-integrating regression with endogeneity and long memory. This project will tackle a number of long-standing technical problems related to non-linear covariance functionals and non-linear transformation of nonstationary time series. This project is intended to provide technical tools for practitioners to study the l .... Non-linear cointegrating regression with endogeneity. This project aims to develop the asymptotic theory of estimation and statistical inference in models concerned with non-linear co-integrating regression with endogeneity and long memory. This project will tackle a number of long-standing technical problems related to non-linear covariance functionals and non-linear transformation of nonstationary time series. This project is intended to provide technical tools for practitioners to study the long-run relationship of economic variables, and could apply to a range of statistical, empirical finance and economic models, enhancing national leadership in these areas.
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    Funded Activity

    Discovery Projects - Grant ID: DP120104014

    Funder
    Australian Research Council
    Funding Amount
    $750,000.00
    Summary
    Development of general methodology for estimating complex time series models. This project will develop novel methods and models for analysing socio-economic and financial data measured over time and will illustrate them with applications. The methods will allow for more efficient and more accurate processing of information and better forecasting which will facilitate better management and more timely policy response.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP200103015

    Funder
    Australian Research Council
    Funding Amount
    $280,000.00
    Summary
    Deep learning based time series modeling and financial forecasting. This project pursues breakthroughs in time series modelling and develops novel statistical models and inference techniques, with a focus on modelling of financial time series data. The advances will be achieved through interdisciplinary research, combining recent advances in machine learning, Bayesian computation, financial econometrics and the increasing availability of Big Data. The outcomes will provide a new range of proven .... Deep learning based time series modeling and financial forecasting. This project pursues breakthroughs in time series modelling and develops novel statistical models and inference techniques, with a focus on modelling of financial time series data. The advances will be achieved through interdisciplinary research, combining recent advances in machine learning, Bayesian computation, financial econometrics and the increasing availability of Big Data. The outcomes will provide a new range of proven and powerful approaches for analysing time series and understanding time effects. The methodologies developed will lead to a greater accuracy in financial forecasting and risk management, and open up new horizons for the wider scientific community to analyse time series data.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP220101043

    Funder
    Australian Research Council
    Funding Amount
    $199,649.00
    Summary
    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.
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    Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE120100748

    Funder
    Australian Research Council
    Funding Amount
    $375,000.00
    Summary
    Robust inference for behavioural models in economics and finance. The project will develop novel methodology to estimate behavioural models in economics and finance, which may give better insights on economic development. Knowledge gained from this project will be useful for Australian industries, banks, investment funds and the government for the effective formulation of their business strategies and policies.
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