Internationally renowned econometrician John Geweke came to UTS as Distinguished Research Professor in the School of Business in 2009. Professor Geweke is distinguished for his contributions to econometric theory in time series analysis and Bayesian modelling, and for applications in the fields of macroeconomics, finance, and microeconomics. He is a Fellow of the Econometric Society and the American Statistical Association. He has been co-editor of the Journal of Econometrics, the Journal of Applied Econometrics, and editor of the Journal of Business and Economic Statistics. His most recent book is Complete and Incomplete Econometric Models, published by Princeton University Press in January 2010. Currently he directs the six-investigator ARC – sponsored project, “Massively Parallel Algorithms for Bayesian Inference and Decision Making.”
Time Series Analysis
Ph.D., Economics, University of Minnesota, 1975
B.S., Social Sciences, Michigan State University, 1970
(2016). Sequentially adaptive Bayesian learning for a nonlinear model of the secular and cyclical behavior of US real GDP. Econometrics. 4(1), doi: 10.3390/econometrics4010010
(2016). Comment on: Reflections on the probability space induced by moment conditions with implications for Bayesian inference. Journal of Financial Econometrics. 14(2), 253-257. doi: 10.1093/jjfinec/nbv011
Bateman, H., Eckert C., Geweke J., Louviere J., Satchell S., & Thorp S.
(2016). Risk presentation and portfolio choice. Review of Finance. 20(1), 201-229. doi: 10.1093/rof/rfv001
Durham, G., Geweke J., & Ghosh P.
(2015). A comment on Christoffersen, Jacobs, and Ornthanalai (2012), “Dynamic jump intensities and risk premiums: Evidence from S&P 500 returns and options”. Journal of Financial Economics. 115(1), 210-214. doi: 10.1016/j.jfineco.2014.08.004