Michael Stanley Smith

Michael Smith has held the Chair of Management in Econometrics at MBS since 2007. He is a leading researcher in Bayesian statistics and business analytics.

Michael completed his PhD at the Australian Graduate School of Management at the University of New South Wales. Prior to joining MBS, he held positions at Monash University and the University of Sydney. He has also held visiting positions at Ludwig Maximilians University in Munich, the Wharton School at the University of Pennsylvania, McCombs School of Business at the University of Texas, London Business School and University College London (UCL).

Past major awards include an Alexander von Humboldt fellowship and an Australian Research Council Future Fellowship. In 2021 he was awarded the University of Melbourne Faculty of Business and Economics Deans' Award for Research Excellence.

Michael’s research focuses on developing methods for the analysis of large and complex datasets that arise in business, economics and elsewhere. On the methodological side, he has worked on Bayesian algorithms, spatial and time series analysis and multivariate modelling. On the applied side, he has worked on marketing models for advertising effectiveness and consumer response, neuroimaging, and macroeconomic and business forecasting. He has a long-standing interest in the electricity markets, including the modelling and forecasting of demand and spot prices.

Michael’s research has published over fifty papers in the leading academic journals in statistics, econometrics, marketing and forecasting. He is regularly invited to speak at international conferences and workshops, and is involved with a number of prominent international academic societies.

Michael has taught courses in econometrics, statistics, decision sciences and business analytics at all levels – from undergraduate to PhD level. At MBS he currently teaches Data Analysis on the part-time MBA and Risk Analytics in the Master of Business Analytics.

Michael is a past director of the Business Administration and Analytics stream of the MBS doctoral program. Much of his research is joint with PhD students and post-doctoral fellows from a wide range of backgrounds. Working with enthusiastic young international researchers is one of the most rewarding aspects of his job. He is also a past Associate Dean of Research.

Select Bibliography

Fu, Y., Smith, M.S. and Panagiotelis, A. (2026), ‘Vector Copula Variational Inference and Dependent Block Posterior Approximations’, Journal of Computational and Graphical Statistics, 35(3), 1102-1116.

Smith, M.S., W. Yu, D.J. Nott and D. Frazier, (2025), ‘Cutting Feedback in Misspecified Copula Models’, Journal of the American Statistical Association, 120(551), 1975–1989.

Deng, L., M.S. Smith, W. Maneesoonthorn, (2025), ‘Large Skew-t Copula Models and Asymmetric Dependence in Intraday Equity Returns’, Journal of Business and Economic Statistics, 43:2, 269-285.

Smith, M.S. and R. Loaiza-Maya, (2023), ‘Implicit copula variational inference’, Journal of Computational and Graphical Statistics, 32:3, 769-781.

Klein, N., M.S. Smith and D.J. Nott, (2023), ‘Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices’, Journal of Applied Econometrics, 38:4, 493 - 511.

Loaiza-Maya, R., M.S. Smith, D.J. Nott and P.J. Danaher, (2022), ‘Fast and Accurate Variational Inference for Models with Many Latent Variables’, Journal of Econometrics, 230:2, 339-362.

Smith, M.S. and N. Klein. (2021), ‘Bayesian Inference for Regression Copulas’, Journal of Business and Economic Statistics, 39:3, 712-728.

Danaher, P.J., T.S. Danaher, M.S. Smith and R. Loaiza-Maya, (2020), ‘Advertising Effectiveness for Multiple Retailer-Brands in a Multimedia and Multichannel Environment’, Journal of Marketing Research, 57:3, 445-467.

Loaiza-Maya, R. and M.S. Smith. (2020). ‘Real Time Macroeconomic Forecasting with a Heteroskedastic Inversion Copula Model’, Journal of Business and Economic Statistics, 38:2, 470-486.

Loaiza-Maya, R. and M.S. Smith, (2019), ‘Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series’, Journal of Computational and Graphical Statistics, 28, 3, 523-539.

Ong, V.M.-H., D.J. Nott and M.S. Smith, (2018). ‘Gaussian variation approximation with a factor covariance structure’, Journal of Computational and Graphical Statistics, 27, 3, 465-478.

Loaiza-Maya, R., M.S. Smith and W. Maneesoonthorn, (2018). ‘Time Series Copulas for Heteroskedastic Data’, Journal of Applied Econometrics, 33, 3, 332 – 354.

Smith, M.S. and S. Vahey, (2016). ‘Asymmetric density forecasts for U.S. macroeconomic variables from a Gaussian copula model of cross-sectional and serial dependence’, Journal of Business and Economic Statistics, 34, 3, 416-434.

Danaher, P.J., M.S. Smith, K. Ranasinghe, and T.S. Danaher, (2015). ‘Where, When, and How Long: Factors That Influence the Redemption of Mobile Phone Coupons’, Journal of Marketing Research, 52, 710-725.

Smith, M.S. (2015). ‘Copula modelling of dependence in multivariate time series’, International Journal of Forecasting, 31:3, 815-833.

Panagiotelis, A., Smith, M.S. and Danaher, P., (2014). ‘From Amazon to Apple: Modeling Online Retail Sales, Purchase Incidence and Visit Behavior’, Journal of Business and Economic Statistics, 32, 1, 14-29.

Smith, M.S. and Khaled, M.A. (2012). ‘Estimation of Copula Models with Discrete Margins via Bayesian Data Augmentation’, Journal of the American Statistical Association, 107, 290-303.

Smith, M.S., Gan, Q. and Kohn, R., (2012). ‘Modeling dependence using skew t copulas: Bayesian inference and applications’, Journal of Applied Econometrics, 27, 500-522

Danaher, P. and Smith, M., (2011), ‘Modeling Multivariate Distributions using Copulas: Applications in Marketing’ (with discussion), Marketing Science, 30, 4-21.

Smith, M., Min, A., Almeida, C., and Czado, C. (2010), ‘Modeling Longitudinal Data using a Pair-Copula Decomposition of Serial Dependence’, Journal of the American Statistical Association, 105, 1467-1479

Panagiotelis, A., and Smith, M., (2008), ‘Bayesian Identification, Selection and Estimation of Functions in High-Dimensional Additive Models’, Journal of Econometrics, 143, 291-316.

Smith, M. and Fahrmeir, L., (2007), ‘Spatial Bayesian variable selection with application to functional magnetic resonance imaging’, Journal of the American Statistical Association, 102, 417-431.

Smith, D. and Smith, M., (2006), ‘Estimation of Binary Markov Random Fields using Markov Chain Monte Carlo’, Journal of Computational and Graphical Statistics, 15, 207-227.

Cottet, R., and Smith, M., (2003), ‘Bayesian modelling and forecasting of intra-day electricity load’, Journal of the American Statistical Association, 98, 839-849.

Smith, M and Kohn, R., (2002), 'Parsimonious Covariance Matrix Estimation for Longitudinal Data', Journal of the American Statistical Association, 97, 460, 1141-1153.

Smith, M., (2000), ‘Short-term forecasting of New South Wales electricity system load’, Journal of Business and Economic Statistics, 18, 465-478.

Smith, M. and Kohn, R., (2000), ‘Nonparametric seemingly unrelated regression’, Journal of Econometrics, 98, 257-281.

Smith, M., Wong, C. and Kohn, R., (1998), ‘Additive nonparametric regression with autocorrelated errors,’ Journal of the Royal Statistical Society B, 60, 311-331

Smith, M. and Kohn, R., (1996), ‘Nonparametric regression using Bayesian variable selection,’ Journal of Econometrics, 75, 317-344