Discovery Early Career Researcher Award - Grant ID: DE200101070

Funding Activity

Website
http://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/DE200101070

Funding Status
Active

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Funded Activity Summary

Consequences of Model Misspecification in Approximate Bayesian Computation. In almost any empirical application, the model the analyst is working with constitutes a misspecified description of the true process that has generated the data. While the method of Approximate Bayesian computation (ABC) is now a staple in the toolkit of the applied modeller, the impact of misspecification in ABC is unknown. This project aims to undertake a rigorous study into the behaviour of ABC under model misspecification. Expected outcomes include new theoretical results for ABC under misspecification and new methods capable of detecting/mitigating model misspecification. This project will provide significant benefits in all spheres where reliable, robust statistical inference methods are required in order to make reliable decisions.

Funded Activity Details

Start Date: 01-02-2020

End Date: 01-02-2024

Funding Scheme: Discovery Early Career Researcher Award

Funding Amount: $376,496.00

Funder: Australian Research Council

Research Topics

ANZSRC Field of Research (FoR)

Econometric and Statistical Methods | Economic Models and Forecasting | Econometrics |

ANZSRC Socio-Economic Objective (SEO)

Expanding Knowledge in Economics | Expanding Knowledge in the Mathematical Sciences

Other Keywords

Econometric and Statistical Methods | Econometrics | Economic Models and Forecasting | Expanding Knowledge in Economics | Expanding Knowledge in the Mathematical Sciences

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