Rate My Professor Loaiza Maya
Ratings & reviews
Makes learning feel effortless and fun.
A true role model for academic success.
Inspires curiosity and a love for knowledge.
Creates a safe and inclusive space.
Helps students see the value in learning.
Encourages creative and innovative thinking.
Taught by or worked with Loaiza?
Add your rating. It takes about 30 seconds.
About Loaiza
Ruben Loaiza-Maya is an Associate Professor in the Department of Econometrics and Business Statistics at Monash Business School, Monash University, a position he has held since July 2020. He earned his undergraduate degree in economics from Universidad Nacional de Colombia in Medellín and completed his doctoral studies in econometrics at the University of Melbourne. His research centers on copula modelling, Bayesian estimation methods, time series analysis, macroeconomic and financial forecasting, with a particular emphasis on developing models and estimation techniques suitable for large datasets featuring multiple highly dependent variables. These efforts address complexities in human decision-making, economic insights from vast information, and practical applications such as assessing currency vulnerability for the Central Bank of Colombia during Latin American exchange rate fluctuations and enhancing forecasts for asset and stock prices using granular intraday data.
In 2023, Loaiza-Maya received the Paul Bourke Award for Early Career Research from the Academy of the Social Sciences in Australia, honoring his substantial contributions as a distinguished econometrician to computational statistics and economics. His influential publications include 'Hybrid unadjusted Langevin methods for high-dimensional latent variable models' (Journal of Econometrics, 2024), 'Bayesian forecasting in economics and finance: a modern review' (International Journal of Forecasting, 2024), 'Fast variational Bayes methods for multinomial probit models' (Journal of Business and Economic Statistics, 2023), 'Variational Bayes in state space models: inferential and predictive accuracy' (Journal of Computational and Graphical Statistics, 2023), 'Fast and accurate variational inference for models with many latent variables' (Journal of Econometrics, 2022), 'Scalable Bayesian estimation in the multinomial probit model' (Journal of Business and Economic Statistics, 2022), 'Focused Bayesian prediction' (Journal of Applied Econometrics, 2021), and 'Advertising effectiveness for multiple retailer-brands in a multimedia and multichannel environment' (Journal of Marketing Research, 2020). He currently leads the project 'Variational Inference for Intractable and Misspecified State Space Models' (2023-2026).