Didier Nibbering

Working papers

• Inference on LATEs with covariates, with Tom Boot

• 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

Publications

• Efficient importance variational approximations for state space models, with Ruben Loaiza-Maya, accepted at  Journal of Business and Economic Statistics

• Random subspace local projections, with Viet Hoang Dinh and Benjamin Wong, forthcoming at The Review of Economics and Statistics

• Instrument-based estimation of full treatment effects with partial compliers, with Matthijs Oosterveen, forthcoming at The Review of Economics and Statistics

• Forecasting carbon emissions using asymmetric grouping, with Richard Paap, Journal of Forecasting, Volume 43, Issue 6, September 2024, Pages 2228-2256

• Hybrid unadjusted Langevin methods for high-dimensional latent variable models, with Ruben Loaiza-Maya and Dan Zhu, Journal of Econometrics, Volume 241, Issue 2, April 2024, Pages 105741 

• A high-dimensional multinomial logit model, Journal of Applied Econometrics, Volume 39, Issue 3, April/May 2024, Pages 481-497

• 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, International Journal of Forecasting, Volume 40, Issue 2, April–June 2024, Pages 811-839

• 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.