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Yong Song

University of Melbourne

Melbourne VIC, Australia
4.40/5 · 5 reviews

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4.008/20/2025

Always kind, respectful, and approachable.

4.005/21/2025

Always clear, concise, and insightful.

5.003/31/2025

Creates a safe space for learning and growth.

4.002/27/2025

Always clear, engaging, and insightful.

5.002/4/2025

Great Professor!

About Yong

Associate Professor Yong Song serves in the Department of Economics, Faculty of Business and Economics at the University of Melbourne. He holds a Ph.D. in Economics from the University of Toronto (2011), an M.A. from Simon Fraser University in Canada, and his undergraduate degree from Nankai University in Tianjin, China. Song joined the University of Melbourne as a lecturer in 2014, advancing to his current position as Associate Professor. He is affiliated with the Bayesian Analysis and Modelling Research Group and specializes in Bayesian Econometrics, Financial Econometrics, and Empirical Macroeconomics.

Song's research encompasses Bayesian nonparametric modelling, financial econometrics, and empirical macroeconomics, with applications to market dynamics, forecasting, and structural changes. Key publications include 'Components of bull and bear markets: bull corrections and bear rallies' in the Journal of Business & Economic Statistics (2012); 'Oil price shocks and economic growth: The volatility link' in the International Journal of Forecasting (2020); 'Identifying speculative bubbles using an infinite hidden Markov model' in the Journal of Financial Econometrics (2015); 'A new structural break model, with an application to Canadian inflation forecasting' in the International Journal of Forecasting (2014); 'Markov switching' (2020); 'Measuring Inflation Expectations Uncertainty Using High-Frequency Data' in the Journal of Money, Credit and Banking (2018); 'An efficient Bayesian approach to multiple structural change in multivariate time series' and 'Sparse change-point VAR models' in the Journal of Applied Econometrics (2018 and 2021); and 'Bull and bear markets during the COVID-19 pandemic' in Finance Research Letters (2021). He contributes as Chief Investigator to Australian Research Council grants, including Discovery Project DP230100959 and Linkage Project LP240100101 on predictive analytics for water consumption.

Professional Email: yong.song@unimelb.edu.au

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