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Encourages creative and innovative thinking.
Always supportive and deeply knowledgeable.
Encourages students to explore new ideas.
Helps students see the value in learning.
Didier Nibbering is an Associate Professor in the Department of Econometrics and Business Statistics within Monash Business School at Monash University in Melbourne, Australia. He earned his PhD in Economics, along with BSc and MSc degrees in Econometrics, from Erasmus University Rotterdam. As a PhD candidate at the Tinbergen Institute affiliated with Erasmus University Rotterdam, Nibbering's doctoral research was recognized with the Christiaan Huygens Science Award in 2021 in the field of mathematics. In 2018, he joined Monash University on a tenure-track Assistant Professor position, advancing to Associate Professor. He has also visited the Department of Statistics at Stanford University and presented his research at various international conferences.
Nibbering's research specializations encompass high-dimensional inference, forecasting methods, Bayesian inference, causal inference, discrete choice models, and forecasting with large-dimensional data. His academic interests lie in developing scalable statistical methods for econometric applications in economics and finance. Key publications include 'A high-dimensional multinomial logit model' in the Journal of Applied Econometrics (2024); 'Bayesian forecasting in economics and finance: a modern review' in the International Journal of Forecasting (2024, co-authored with G.M. Martin et al.); 'Forecasting using random subspace methods' in the Journal of Econometrics (2019, with T. Boot); 'Forecasting carbon emissions using asymmetric grouping' in the Journal of Forecasting (2024, with R. Paap); 'Hybrid unadjusted Langevin methods for high-dimensional latent variable models' in the Journal of Econometrics (2024, with R. Loaiza-Maya and D. Zhu); 'Scalable Bayesian estimation in the multinomial probit model' in the Journal of Business and Economic Statistics (2022, with R. Loaiza-Maya); and 'Multiclass-penalized logistic regression' in Computational Statistics & Data Analysis (2022, with T.J. Hastie). These contributions have appeared in premier outlets, demonstrating his influence in advancing methodologies for high-dimensional econometric modeling and probabilistic forecasting.