Didier Nibbering
I am a Senior Lecturer (Assistant Professor) at the Department of Econometrics and Business Statistics at Monash University.
Contact Information:
didier.nibbering[at]monash.edu
Department of Econometrics and Business Statistics
Room 243, 29 Ancora Imparo Way
Clayton, VIC 3168, Australia
Working papers
Instrument-based estimation of full treatment effects with movers, with Matthijs Oosterveen
Random subspace local projections, with Viet Hoang Dinh and Benjamin Wong
Hybrid unadjusted Langevin methods for high-dimensional latent variable models, with Ruben Loaiza-Maya and Dan Zhu
Efficient variational approximations for state space models, with Ruben Loaiza-Maya
Clustered local average treatment effects: fields of study and academic student progress, with Matthijs Oosterveen and Pedro Luis Silva
The Tale of the Tail: Inference for Customer Purchase Behavior in the Long Tail, with Bruno Jacobs
Panel Forecasting with Asymmetric Grouping, with Richard Paap
Publications
A high-dimensional multinomial logit model, accepted at Journal of Applied Econometrics.
Bayesian Forecasting in the 21st Century: A Modern Review, with Gael M. Martin, David T. Frazier, Worapree Maneesoonthorn, Ruben Loaiza-Maya, Florian Huber, Gary Koop, John Maheu, and Anastasios Panagiotelis, accepted at International Journal of Forecasting.
Fast variational Bayes methods for multinomial probit models, with Ruben Loaiza-Maya, Journal of Business and Economic Statistics, Volume 41, Issue 4, October 2023, Pages 1352-1363.
Scalable Bayesian estimation in the multinomial probit model, with Ruben Loaiza-Maya, Journal of Business and Economic Statistics, Volume 40, Issue 4, October 2022, Pages 1678-1690.
Multiclass-penalized logistic regression, with Trevor Hastie, Computational Statistics and Data Analysis, Volume 169, May 2022, Pages 107414.
Subspace methods, with Tom Boot, In P. Fuleky (Ed.), 2020 Macroeconomic Forecasting in the Era of Big Data: Theory and Practice (pp. 269-291). (Advanced Studies in Theoretical and Applied Econometrics; Vol. 52). Cham: Springer.
Forecasting using Random Subspace Methods, with Tom Boot, Journal of Econometrics, Volume 209, Issue 2, April 2019, Pages 391-406.
What do Professional Forecasters actually predict? with Richard Paap and Michel van der Wel, International Journal of Forecasting, Volume 34, Issue 2, April–June 2018, Pages 288-311.