Faculty staff profile

Michael Smith joined MBS in 2007, after eight years as an Associate Professor in the Discipline of Econometrics and Business Statistics at the University of Sydney. During that time he also held visiting positions at the University of Munich and The Wharton School at the University of Pennsylvania.

Michael's research is evenly balanced between the development of econometric and statistical models and methods, and their application to solve problems arising in business and elsewhere. He is prominent internationally for his work on Bayesian semiparametric modeling and model averaging in cross-sectional, spatial and time series contexts.

His work has appeared widely in top journals in econometrics and statistics, including the Journal of Econometrics, Journal of the American Statistical Association, Journal of Business and Economics Statistics, Journal of the Royal Statistical Society B and the Journal of Computational and Graphical Statistics.

He has consulted widely in the energy industry, with a focus on the modeling and forecasting of both demand and spot prices within deregulated wholesale electricity markets.

Recent publications and working papers

• Bayesian Skew Selection for Multivariate Models
• Additive nonparametric regression with autocorrelated errors
• Semiparametric Regression: An Exposition and Application to Print Advertising Data
• Modeling Multivariate Distributions Using Copulas: Applications in Marketing
• Nonparametric seemingly unrelated regression
• Short-term forecasting of New South Wales electricity system load
• Nonparametric regression using linear combinations of basis functions
• Estimating long term trends in tropospheric ozone
• 'Parsimonious Covariance Matrix Estimation for Longitudinal Data
• Assessing brain activity using spatial Bayesian variable selection
• Bayesian modelling and forecasting of intra-day electricity load
• Estimation of Binary Markov Random Fields using Markov Chain Monte Carlo
• Foreign exchange intervention by the Bank of Japan: Bayesian analysis using a bivariate stochastic volatility model
• Spatial Bayesian variable selection with application to functional magnetic resonance imaging
• Bayesian Density Forecasting of Intraday Electricity Prices using Multivariate Skew t Distributions
• Bayesian Identification, Selection and Estimation of Functions in High-Dimensional Additive Models
• View all Michael's working papers & publications