JOE Listings (Job Openings for Economists)
August 1, 2021 - January 31, 2022
Position Title/Short Description
Section: International: Other Academic (Visiting or Temporary)
Location: Clayton, Victoria, AUSTRALIA
JEL Classification: 00 -- 00 - Default: Any Field
Full Text of JOE Listing:
Job No.: 627965
Location: Clayton campus
Employment Type: Full-time
Duration: 12-month fixed-term appointment
Remuneration: AUD $103,153 - $122,495 pa (plus 10% employer superannuation)
The Department of Econometrics and Business Statistics, one of seven academic departments in the Monash Business School, comprises approximately 50 academics with particular strengths in econometric theory and methods, Bayesian inference and computation, applied econometrics, time series analysis, forecasting, statistics, actuarial science, data visualisation and analytics, and machine learning.
In the Excellence in Research for Australia assessments conducted by the Australian Research Council in 2012, 2015 and 2018, Monash University received a rank of 5, the highest possible rank, in Econometrics. The Monash Business School is also in the top 10% of institutions in Econometrics, Time Series and Forecasting as ranked by IDEAS (a Research Papers in Economics service maintained by the Federal Reserve Bank of St. Louis, USA), meaning that the Department appears among the best institutions in the world.
The Research Fellow will conduct research associated with ARC Discovery Grant DP200101414: “Loss-Based Bayesian Prediction”. This project proposes a new paradigm for prediction. Using state-of-the-art computational methods, the project aims to produce accurate, fit for purpose predictions which, by design, reduce the loss incurred when the prediction is inaccurate. Theoretical validation of the new predictive method is an expected outcome, as is extensive application of the method to diverse empirical problems, including those based on high-dimensional and hierarchical data sets. The project will exploit recent advances in Bayesian computation, including approximate Bayesian computation and variational inference, to produce predictive distributions that are expressly designed to yield accurate predictions in a given loss measure. The Research Fellow would be expected to engage in all aspects of the research and would therefore build expertise in the methodological, theoretical and empirical aspects of this new predictive approach.
The appointee will have a doctoral qualification in econometrics or statistics, with specific expertise in one or more of the following areas: Bayesian statistical methods, including modern computational techniques; forecast methodology and/or theory; high-dimensional statistical analysis; statistical theory.
To view the position description and for instructions on how to apply, please click on the application link below.
Professor Gael Martin, Chief Investigator, +61 3 9905 1189
Tuesday 15 February 2022, 11:55 pm AEDT
- External Application Link