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Discovery Projects - Grant ID: DP150101728

Title
Discovery Projects - Grant ID: DP150101728
Funding
ARC | Discovery Projects
Contract (GA) number
DP150101728
Start Date
2015/01/01
End Date
2017/12/31
Open Access mandate
no
Organizations
-
More information
http://purl.org/au-research/grants/arc/DP150101728

 

  • A Robust Bayesian Exponentially Tilted Empirical Likelihood Method

    Liu, Zhichao; Forbes, Catherine S.; Anderson, Heather M. (2017)
    Projects: ARC | Discovery Projects - Grant ID: DP150101728 (DP150101728)
    This paper proposes a new Bayesian approach for analysing moment condition models in the situation where the data may be contaminated by outliers. The approach builds upon the foundations developed by Schennach (2005) who proposed the Bayesian exponentially tilted empirical likelihood (BETEL) method, justified by the fact that an empirical likelihood (EL) can be interpreted as the nonparametric limit of a Bayesian procedure when the implied probabilities are obtained from maximizing entropy s...

    Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models

    Martin, Gael M.; McCabe, Brendan P. M.; Frazier, David T.; Maneesoonthorn, Worapree; Robert, Christian P. (2016)
    Projects: ARC | Discovery Projects - Grant ID: DP150101728 (DP150101728)
    A new approach to inference in state space models is proposed, using approximate Bayesian computation (ABC). ABC avoids evaluation of an intractable likelihood by matching summary statistics computed from observed data with statistics computed from data simulated from the true process, based on parameter draws from the prior. Draws that produce a 'match' between observed and simulated summaries are retained, and used to estimate the inaccessible posterior; exact inference being feasible only ...

    Approximate Bayesian Forecasting

    Frazier, David T.; Maneesoonthorn, Worapree; Martin, Gael M.; McCabe, Brendan P. M. (2017)
    Projects: ARC | Discovery Projects - Grant ID: DP150101728 (DP150101728), ARC | Discovery Projects - Grant ID: DP170100729 (DP170100729)
    Approximate Bayesian Computation (ABC) has become increasingly prominent as a method for conducting parameter inference in a range of challenging statistical problems, most notably those characterized by an intractable likelihood function. In this paper, we focus on the use of ABC not as a tool for parametric inference, but as a means of generating probabilistic forecasts; or for conducting what we refer to as `approximate Bayesian forecasting'. The four key issues explored are: i) the link b...

    Dynamic Asset Price Jumps and the Performance of High Frequency Tests and Measures

    Maneesoonthorn, Worapree; Martin, Gael M.; Forbes, Catherine S. (2017)
    Projects: ARC | Discovery Projects - Grant ID: DP170100729 (DP170100729), ARC | Discovery Projects - Grant ID: DP150101728 (DP150101728)
    This paper provides an extensive evaluation of high frequency jump tests and measures, in the context of dynamic models for asset price jumps. Specifically, we investigate: i) the power of alternative tests to detect individual price jumps, including in the presence of volatility jumps; ii) the frequency with which sequences of dynamic jumps are identified; iii) the accuracy with which the magnitude and sign of sequential jumps are estimated; and iv) the robustness of inference about dynamic ...
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  • Scientific Results

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    PUBLICATIONS BY ACCESS MODE

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