Personal profile
Personal information
Siem Jan Koopman is Professor of Econometrics at the Department of Econometrics, Vrije Universiteit Amsterdam. He is also a research fellow at Tinbergen Institute and a long-term Visiting Professor at CREATES, University of Aarhus. Furthermore, he is a Journal of Applied Econometrics Distinguished Author, and Fellow of the Society of Financial Econometrics (SoFiE).
He held positions at London School of Economics and CentER (Tilburg University), and had long-term visits at US Bureau of the Census, European University Institute, and European Central Bank, Financial Research.
The monograph Time Series Analysis by State Space Methods is written by J. Durbin and SJK. The book originally appeared in 2001, the Second Edition in 2012. The book An Introduction to State Space Time Series Analysis appeared in 2007 and is written by J.J.F. Commandeur and SJK. His other books (co-authored, software and editorial) are listed here.
He is a Statistical Software Developer: STAMP, SsfPack.
Research
The research interests of SJK cover topics in time series econometrics, financial econometrics, forecasting and simulation-based estimation. His current research focusses on score-driven time-varying parameter models (GAS models), state space models and dynamic factor models. He fullfills editorial duties at Journal of Business and Economic Statistics, Journal of Applied Econometrics, and Journal of Forecasting. Finally, SJK is an OxMetrics software developer for STAMP and SsfPack.
For more information see: http://sjkoopman.net/
Teaching
Time Series Econometrics -- VU Amsterdam, MSc Econometrics
Advanced Econometrics III -- Tinbergen Institute, MPhil
Ancillary activities
- SsfPack Consulting B.V. | Amsterdam | Directeur | 2002-03-01 - present
Ancillary activities are updated daily
Academic qualification
PhD, Department of Statistics, The London School of Economics and Political Science
1 Sept 1989 → 30 Mar 1992
Award Date: 30 Mar 1992
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 17 Partnerships for the Goals
Collaborations and top research areas from the last five years
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Score-driven time-varying parameter models with spline-based densities
van Brummelen, J., Gorgi, P. & Koopman, S. J., Apr 2026, In: Statistics and Computing. 36, 2, 94.Research output: Contribution to Journal › Article › Academic › peer-review
Open Access -
Extremum Monte Carlo Filters: Signal Extraction via Simulation and Regression
Moussa, K., Blasques, F. & Koopman, S. J., 12 Jan 2026, (E-pub ahead of print) In: Journal of Business and Economic Statistics.Research output: Contribution to Journal › Article › Academic › peer-review
Open Access -
Time-varying correlations in multivariate unobserved components time series models
Schiavoni, C., Koopman, S. J., Palm, F., Smeekes, S. & Van den Brakel, J., Jan 2026, In: Journal of the Royal Statistical Society. Series A: Statistics in Society. 189, 1, p. 292-313 22 p.Research output: Contribution to Journal › Article › Academic › peer-review
Open Access -
Data assimilation with extremum Monte Carlo methods
Moussa, K. & Koopman, S. J., 9 Dec 2025, (E-pub ahead of print) In: Quarterly Journal of the Royal Meteorological Society.Research output: Contribution to Journal › Article › Academic › peer-review
Open Access -
Asymmetric stable stochastic volatility models: estimation, filtering, and forecasting
Blasques, F., Koopman, S. J. & Moussa, K., Nov 2025, In: Journal of Time Series Analysis. 46, 6, p. 1098-1124 27 p.Research output: Contribution to Journal › Article › Academic › peer-review
Open Access
Courses
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Practical Case Study: Real-life Modelling in Econometrics and Data Science
van de Werve, I. 1/09/25 → 31/08/26
Course
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Aarhus University, Denmark (External organisation)
Koopman, S. J. (Member)
2016 → 2017Activity: Membership › Academic
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Society of Financial Econometrics (External organisation)
Koopman, S. J. (Member)
2016 → 2017Activity: Membership › Academic
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Journal of Business and Economic Statistics (Journal)
Koopman, S. J. (Member of editorial board)
2015 → 2020Activity: Peer review and Editorial work › Editorial work › Academic
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Predicting time-varying parameters with parameter-driven and observation-driven models
Koopman, S. J. (Speaker)
18 Jan 2013Activity: Lecture / Presentation › Academic
Prizes / Grants
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Labex Louis Bachelier grant (10K Euro) from the Institut Europlace de Finance (EIF) to conduct research in portfolio allocation
Koopman, S. J. (Recipient), 2015
Prize / Grant: Grant › Academic
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Research grant from the National Bank of Poland (10K Euro) to develop models for the forecasting of interest rates
Koopman, S. J. (Recipient), Lucas, A. (Recipient) & Zamojski, M. J. (Recipient), 2015
Prize / Grant: Grant › Academic
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Systemic Risk Tomography
Lucas, A. (Recipient), Koopman, S. J. (Recipient) & Siegmann, A. H. (Recipient), 2013
Prize / Grant: Grant › Academic
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The VILLUM Visiting Professor Programme grant from the Velux Foundation (60k Euro)
Koopman, S. J. (Recipient), 2015
Prize / Grant: Grant › Academic
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VILLUM Visiting Professor Programme Grant (60K euro) 2016-2017
Koopman, S. J. (Recipient), 2016
Prize / Grant: Grant › Academic
Datasets
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Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models
Koopman, S. J. (Contributor), Lucas, A. (Contributor) & Scharth, M. (Contributor), Unknown, 1 Jan 2015
DOI: 10.6084/m9.figshare.1054782.v1, https://tandf.figshare.com/articles/dataset/Numerically_Accelerated_Importance_Sampling_for_Nonlinear_Non_Gaussian_State_Space_Models_a_href_fn0001_target_blank_a_/1054782/1
Dataset / Software: Dataset
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Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State-Space Models
Koopman, S. J. (Contributor), Lucas, A. (Contributor) & Scharth, M. (Contributor), Unknown, 1 Jan 2015
DOI: 10.6084/m9.figshare.1054782.v2, https://tandf.figshare.com/articles/dataset/Numerically_Accelerated_Importance_Sampling_for_Nonlinear_Non_Gaussian_State_Space_Models/1054782/2
Dataset / Software: Dataset