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Didier Nibbering

Personal Details

First Name:Didier
Middle Name:
Last Name:Nibbering
Suffix:
RePEc Short-ID:pni475
[This author has chosen not to make the email address public]
http://didiernibbering.com

Affiliation

Department of Econometrics and Business Statistics
Monash Business School
Monash University

Melbourne, Australia
http://business.monash.edu/econometrics-and-business-statistics
RePEc:edi:dxmonau (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Didier Nibbering & Matthijs Oosterveen, 2025. "Policy-relevant causal effect estimation using instrumental variables with interference," Papers 2509.12538, arXiv.org.
  2. Akanksha Negi & Didier Nibbering, 2025. "Identification of dynamic treatment effects when treatment histories are partially observed," Papers 2501.04853, arXiv.org, revised Jun 2025.
  3. Viet Hoang Dinh & Didier Nibbering & Benjamin Wong, 2024. "Random Subspace Local Projections," Papers 2406.01002, arXiv.org.
  4. Tom Boot & Didier Nibbering, 2024. "Inference on LATEs with covariates," Papers 2402.12607, arXiv.org, revised Nov 2024.
  5. Ruben Loaiza-Maya & Didier Nibbering & Dan Zhu, 2023. "Hybrid unadjusted Langevin methods for high-dimensional latent variable models," Papers 2306.14445, arXiv.org.
  6. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
  7. Didier Nibbering, 2023. "A High-dimensional Multinomial Logit Model," Monash Econometrics and Business Statistics Working Papers 19/23, Monash University, Department of Econometrics and Business Statistics.
  8. Didier Nibbering & Matthijs Oosterveen, 2023. "Instrument-based estimation of full treatment effects with movers," Papers 2306.07018, arXiv.org.
  9. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
  10. Rub'en Loaiza-Maya & Didier Nibbering, 2022. "Efficient variational approximations for state space models," Papers 2210.11010, arXiv.org, revised Jun 2023.
  11. Rub'en Loaiza-Maya & Didier Nibbering, 2022. "Fast variational Bayes methods for multinomial probit models," Papers 2202.12495, arXiv.org, revised Oct 2022.
  12. Nibbering, Didier & Oosterveen, Matthijs & Silva, Pedro Luís, 2022. "Clustered Local Average Treatment Effects: Fields of Study and Academic Student Progress," IZA Discussion Papers 15159, Institute of Labor Economics (IZA).
  13. Didier Nibbering & Coos van Buuren & Wei Wei, 2021. "Real Options Valuation of Wind Energy Based on the Empirical Production Uncertainty," Monash Econometrics and Business Statistics Working Papers 19/21, Monash University, Department of Econometrics and Business Statistics.
  14. Ruben Loaiza-Maya & Didier Nibbering, 2020. "Scalable Bayesian estimation in the multinomial probit model," Papers 2007.13247, arXiv.org, revised Mar 2021.
  15. Nibbering, D. & Paap, R., 2019. "Panel Forecasting with Asymmetric Grouping," Econometric Institute Research Papers EI-2019-30, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  16. Didier Nibbering, 2019. "A High-dimensional Multinomial Choice Model," Monash Econometrics and Business Statistics Working Papers 19/19, Monash University, Department of Econometrics and Business Statistics.
  17. Tom Boot & Didier Nibbering, 2017. "Inference in high-dimensional linear regression models," Tinbergen Institute Discussion Papers 17-032/III, Tinbergen Institute, revised 05 Jul 2017.
  18. Tom Boot & Didier Nibbering, 2016. "Forecasting Using Random Subspace Methods," Tinbergen Institute Discussion Papers 16-073/III, Tinbergen Institute, revised 11 Aug 2017.
  19. Didier Nibbering & Richard Paap & Michel van der Wel, 2016. "A Bayesian Infinite Hidden Markov Vector Autoregressive Model," Tinbergen Institute Discussion Papers 16-107/III, Tinbergen Institute, revised 13 Oct 2017.
  20. Didier Nibbering & Richard Paap & Michel van der Wel, 2015. "What Do Professional Forecasters Actually Predict?," Tinbergen Institute Discussion Papers 15-095/III, Tinbergen Institute, revised 13 Oct 2017.

Articles

  1. Didier Nibbering, 2024. "A high‐dimensional multinomial logit model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 481-497, April.
  2. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
  3. Loaiza-Maya, Rubén & Nibbering, Didier & Zhu, Dan, 2024. "Hybrid unadjusted Langevin methods for high-dimensional latent variable models," Journal of Econometrics, Elsevier, vol. 241(2).
  4. Didier Nibbering & Richard Paap, 2024. "Forecasting carbon emissions using asymmetric grouping," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2228-2256, September.
  5. Rubén Loaiza-Maya & Didier Nibbering, 2023. "Fast Variational Bayes Methods for Multinomial Probit Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1352-1363, October.
  6. Nibbering, Didier & Hastie, Trevor J., 2022. "Multiclass-penalized logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
  7. Rubén Loaiza-Maya & Didier Nibbering, 2022. "Scalable Bayesian Estimation in the Multinomial Probit Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1678-1690, October.
  8. Boot, Tom & Nibbering, Didier, 2019. "Forecasting using random subspace methods," Journal of Econometrics, Elsevier, vol. 209(2), pages 391-406.
  9. Nibbering, Didier & Paap, Richard & van der Wel, Michel, 2018. "What do professional forecasters actually predict?," International Journal of Forecasting, Elsevier, vol. 34(2), pages 288-311.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Viet Hoang Dinh & Didier Nibbering & Benjamin Wong, 2024. "Random Subspace Local Projections," Papers 2406.01002, arXiv.org.

    Cited by:

    1. Ho, Paul & Lubik, Thomas A. & Matthes, Christian, 2024. "Averaging impulse responses using prediction pools," Journal of Monetary Economics, Elsevier, vol. 146(C).

  2. Tom Boot & Didier Nibbering, 2024. "Inference on LATEs with covariates," Papers 2402.12607, arXiv.org, revised Nov 2024.

    Cited by:

    1. Dennis Lim & Wenjie Wang & Yichong Zhang, 2024. "A Dimension-Agnostic Bootstrap Anderson-Rubin Test For Instrumental Variable Regressions," Papers 2412.01603, arXiv.org, revised Sep 2025.

  3. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.

    Cited by:

    1. Florindo, Joao B. & Lima, Reneé Rodrigues & dos Santos, Francisco Alves & Alves, Jerson Leite, 2025. "GHENet: Attention-based Hurst exponents for the forecasting of stock market indexes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 667(C).
    2. Ferreira Batista Martins, Igor & Virbickaitè, Audronè & Nguyen, Hoang & Freitas Lopes, Hedibert, 2025. "Volume-driven time-of-day effects in intraday volatility models," Working Papers 2025:14, Örebro University, School of Business.
    3. Maximilian Boeck & Massimiliano Marcellino & Michael Pfarrhofer & Tommaso Tornese, 2024. "Predicting Tail-Risks for the Italian Economy," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 20(3), pages 339-366, November.
    4. Tony Chernis & Gary Koop & Emily Tallman & Mike West, 2024. "Decision synthesis in monetary policy," Papers 2406.03321, arXiv.org, revised Feb 2025.
    5. John T. Rickard & William A. Dembski & James Rickards, 2025. "An Interval Type-2 Version of Bayes Theorem Derived from Interval Probability Range Estimates Provided by Subject Matter Experts," Papers 2509.08834, arXiv.org.
    6. Mukta Mani, 2025. "An Exploration of Contemporary Trends in Finance Research," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(3), pages 12291-12316, September.
    7. A. Ford Ramsey & Michael K. Adjemian, 2024. "Forecast combination in agricultural economics: Past, present, and the future," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 46(4), pages 1450-1478, December.
    8. Hamid Ahaggach & Lylia Abrouk & Eric Lebon, 2024. "Systematic Mapping Study of Sales Forecasting: Methods, Trends, and Future Directions," Forecasting, MDPI, vol. 6(3), pages 1-31, July.

  4. Rub'en Loaiza-Maya & Didier Nibbering, 2022. "Fast variational Bayes methods for multinomial probit models," Papers 2202.12495, arXiv.org, revised Oct 2022.

    Cited by:

    1. Ruben Loaiza-Maya & Didier Nibbering & Dan Zhu, 2023. "Hybrid unadjusted Langevin methods for high-dimensional latent variable models," Papers 2306.14445, arXiv.org.
    2. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    3. Lin Deng & Michael Stanley Smith & Worapree Maneesoonthorn, 2023. "Large Skew-t Copula Models and Asymmetric Dependence in Intraday Equity Returns," Papers 2308.05564, arXiv.org, revised Jul 2024.

  5. Nibbering, Didier & Oosterveen, Matthijs & Silva, Pedro Luís, 2022. "Clustered Local Average Treatment Effects: Fields of Study and Academic Student Progress," IZA Discussion Papers 15159, Institute of Labor Economics (IZA).

    Cited by:

    1. Eskil Heinesen & Christian Hvid & Lars Johannessen Kirkebøen & Edwin Leuven & Magne Mogstad, 2022. "Instrumental Variables with Unordered Treatments: Theory and Evidence from Returns to Fields of Study," NBER Working Papers 30574, National Bureau of Economic Research, Inc.
    2. Sokbae Lee & Bernard Salani'e, 2020. "Treatment Effects with Targeting Instruments," Papers 2007.10432, arXiv.org, revised Dec 2024.
    3. Manudeep Bhuller & Henrik Sigstad, 2022. "2SLS with Multiple Treatments," Papers 2205.07836, arXiv.org, revised May 2024.

  6. Ruben Loaiza-Maya & Didier Nibbering, 2020. "Scalable Bayesian estimation in the multinomial probit model," Papers 2007.13247, arXiv.org, revised Mar 2021.

    Cited by:

    1. Ruben Loaiza-Maya & Didier Nibbering & Dan Zhu, 2023. "Hybrid unadjusted Langevin methods for high-dimensional latent variable models," Papers 2306.14445, arXiv.org.
    2. Zhentong Lu & Kenichi Shimizu, 2025. "Estimating Discrete Choice Demand Models with Sparse Market-Product Shocks," Working Papers 2025-01, University of Alberta, Department of Economics.
    3. Patrick Ding & Guido Imbens & Zhaonan Qu & Yinyu Ye, 2024. "Computationally Efficient Estimation of Large Probit Models," Papers 2407.09371, arXiv.org, revised Sep 2024.
    4. Rub'en Loaiza-Maya & Didier Nibbering, 2022. "Fast variational Bayes methods for multinomial probit models," Papers 2202.12495, arXiv.org, revised Oct 2022.
    5. Michelle Sovinsky & Liana Jacobi & Alessandra Allocca & Tao Sun, 2024. "More than Joints: Multi-Substance Use, Choice Limitations, and Policy Implications," CRC TR 224 Discussion Paper Series crctr224_2024_501, University of Bonn and University of Mannheim, Germany.
    6. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    7. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    8. Michelle Sovinsky & Liana Jacobi & Alessandra Allocca & Tao Sun, 2023. "More than Joints: Multi-Substance Use, Choice Limitations, and Policy Implications," Rationality and Competition Discussion Paper Series 487, CRC TRR 190 Rationality and Competition.
    9. Riccardo Lucchetti & Luca Pedini, 2025. "Correction to: The Spherical Parametrisation for Correlation Matrices and its Computational Advantages," Computational Economics, Springer;Society for Computational Economics, vol. 65(4), pages 2449-2450, April.
    10. Tao Sun, 2024. "Bundle Choice Model with Endogenous Regressors: An Application to Soda Tax," Papers 2412.05794, arXiv.org.

  7. Didier Nibbering, 2019. "A High-dimensional Multinomial Choice Model," Monash Econometrics and Business Statistics Working Papers 19/19, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Nibbering, Didier & Hastie, Trevor J., 2022. "Multiclass-penalized logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).

  8. Tom Boot & Didier Nibbering, 2016. "Forecasting Using Random Subspace Methods," Tinbergen Institute Discussion Papers 16-073/III, Tinbergen Institute, revised 11 Aug 2017.

    Cited by:

    1. Tom Boot & Bart Keijsers, 2025. "Diffusion index forecasts under weaker loadings: PCA, ridge regression, and random projections," Papers 2506.09575, arXiv.org.
    2. Mohitosh Kejriwal & Xuewen Yu, 2019. "Generalized Forecasr Averaging in Autoregressions with a Near Unit Root," Purdue University Economics Working Papers 1318, Purdue University, Department of Economics.
    3. Zichuan Guo & Mihai Cucuringu & Alexander Y. Shestopaloff, 2025. "Generalized Factor Neural Network Model for High-dimensional Regression," Papers 2502.11310, arXiv.org, revised Mar 2025.
    4. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    5. Kozyrev, Boris, 2024. "Forecast combination and interpretability using random subspace," IWH Discussion Papers 21/2024, Halle Institute for Economic Research (IWH).
    6. Chen, Bin & Maung, Kenwin, 2023. "Time-varying forecast combination for high-dimensional data," Journal of Econometrics, Elsevier, vol. 237(2).
    7. Maddalena Cavicchioli, 2025. "Forecasting Markov switching vector autoregressions: Evidence from simulation and application," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 136-152, January.
    8. Cheng-Feng Wu & Shian-Chang Huang & Chei-Chang Chiou & Tsangyao Chang & Yung-Chih Chen, 2022. "The Relationship Between Economic Growth and Electricity Consumption: Bootstrap ARDL Test with a Fourier Function and Machine Learning Approach," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1197-1220, December.
    9. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic Forecast Accuracy in data-rich environment," Post-Print hal-02435757, HAL.
    10. Dennis Kant & Andreas Pick & Jasper de Winter, 2022. "Nowcasting GDP using machine learning methods," Working Papers 754, DNB.
    11. Robert-Paul Berben & Rajni Rasiawan & Jasper de Winter, 2025. "Forecasting Dutch inflation using machine learning methods," Working Papers 828, DNB.
    12. Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
    13. Younker, James, 2025. "Calculating effective degrees of freedom for forecast combinations and ensemble models," Economics Letters, Elsevier, vol. 247(C).

  9. Didier Nibbering & Richard Paap & Michel van der Wel, 2015. "What Do Professional Forecasters Actually Predict?," Tinbergen Institute Discussion Papers 15-095/III, Tinbergen Institute, revised 13 Oct 2017.

    Cited by:

    1. Feunou Bruno & Fontaine Jean-Sébastien & Jin Jianjian, 2021. "What model for the target rate," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(1), pages 1-23, February.

Articles

  1. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
    See citations under working paper version above.
  2. Rubén Loaiza-Maya & Didier Nibbering, 2023. "Fast Variational Bayes Methods for Multinomial Probit Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1352-1363, October.
    See citations under working paper version above.
  3. Nibbering, Didier & Hastie, Trevor J., 2022. "Multiclass-penalized logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).

    Cited by:

    1. Aaron J. Molstad & Keshav Motwani, 2023. "Multiresolution categorical regression for interpretable cell‐type annotation," Biometrics, The International Biometric Society, vol. 79(4), pages 3485-3496, December.

  4. Rubén Loaiza-Maya & Didier Nibbering, 2022. "Scalable Bayesian Estimation in the Multinomial Probit Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1678-1690, October.
    See citations under working paper version above.
  5. Boot, Tom & Nibbering, Didier, 2019. "Forecasting using random subspace methods," Journal of Econometrics, Elsevier, vol. 209(2), pages 391-406.
    See citations under working paper version above.
  6. Nibbering, Didier & Paap, Richard & van der Wel, Michel, 2018. "What do professional forecasters actually predict?," International Journal of Forecasting, Elsevier, vol. 34(2), pages 288-311.
    See citations under working paper version above.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 22 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (17) 2016-09-11 2016-12-11 2017-03-26 2019-09-16 2019-10-28 2020-08-24 2022-05-02 2022-05-09 2022-11-28 2023-05-01 2023-07-17 2023-08-14 2023-08-21 2023-12-04 2024-04-01 2025-01-20 2025-09-22. Author is listed
  2. NEP-DCM: Discrete Choice Models (7) 2019-10-28 2020-08-24 2020-08-31 2022-05-09 2022-11-28 2023-08-14 2023-12-04. Author is listed
  3. NEP-FOR: Forecasting (6) 2015-08-25 2016-09-11 2016-12-11 2019-09-16 2023-01-16 2023-05-01. Author is listed
  4. NEP-ORE: Operations Research (6) 2016-12-11 2019-09-16 2019-10-28 2020-08-24 2021-11-22 2022-05-09. Author is listed
  5. NEP-ETS: Econometric Time Series (5) 2016-09-11 2016-12-11 2022-11-28 2023-01-16 2023-08-21. Author is listed
  6. NEP-CMP: Computational Economics (2) 2023-05-01 2023-08-21
  7. NEP-UPT: Utility Models and Prospect Theory (2) 2020-08-24 2023-12-04
  8. NEP-BIG: Big Data (1) 2024-07-15
  9. NEP-ENE: Energy Economics (1) 2021-11-22
  10. NEP-EXP: Experimental Economics (1) 2022-05-02
  11. NEP-INV: Investment (1) 2024-04-01
  12. NEP-MAC: Macroeconomics (1) 2015-08-25

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