Discovery Projects - Grant ID: DP0343650

Funding Activity

Website
http://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/DP0343650

Funding Status
Closed

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Funded Activity Summary

Efficient pooling of cross-section and time series data using Bayesian machine learning with two econometric applications. In this project, we adapt a Bayesian modelling strategy, namely the minimum message length principle, to the problem of efficient partitioning of economic units, such as firms or countries, into groups whose behavioural patterns are similar within each group but distinct across groups. This methodology can incorporate the requirements of economic theory. The resulting software will be developed for the Web. We consider two specific applications, namely modelling gasoline demand in OECD countries, and finding the foreign factor with the most predictive power for the growth rate of the Australian economy. The second application is of considerable national interest.

Funded Activity Details

Start Date: 14-04-2003

End Date: 30-09-2007

Funding Scheme: Discovery Projects

Funding Amount: $107,250.00

Funder: Australian Research Council

Research Topics

ANZSRC Field of Research (FoR)

Econometric And Statistical Methods | Panel Data Analysis | Econometrics | Coding And Information Theory

ANZSRC Socio-Economic Objective (SEO)

Macroeconomic issues not elsewhere classified | Application packages | Economic issues not elsewhere classified |

Other Keywords

Application packages | Coding And Information Theory | Econometric And Statistical Methods | Econometrics | Economic issues not elsewhere classified | Macroeconomic issues not elsewhere classified | Panel Data Analysis

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