Siem Jan Koopman

Siem Jan Koopman is Professor of Econometrics at the Vrije Universiteit Amsterdam and research fellow at the Tinbergen Institute, both since 1999. He is currently also a Regular Visiting Researcher at the European Central Bank. He held positions at the London School of Economics between 1992 and 1997 and at the CentER (Tilburg University) between 1997 and 1999. In 2002 he visited the US Bureau of the Census in Washington DC as an ASA / NSF / US Census / BLS Research Fellow. Further he was a Fernand Braudel Senior Fellow at the Department of Economics, European University Institute, Florence, Italy, in 2010. His Ph.D. is from the LSE and dates back to 1992.

He is actively engaged in various research projects and has published many articles in leading journals. 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 is published by Oxford University Press in 2012. The research interests of SJK are statistical analysis of time series, financial econometrics, simulation-based estimation, Kalman filter, economic forecasting and other topics in time series econometrics. He fullfills editorial duties at the Journal of Applied Econometrics and the Journal of Forecasting. Finally he is an OxMetrics software developer and is actively engaged in the development of the time series software packages STAMP and SsfPack.

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Blasques, F., Koopman, S. J. & Nientker, M. (2022). A time-varying parameter model for local explosions. Journal of Econometrics, 227(1), 65-84. https://doi.org/10.1016/j.jeconom.2021.05.008

Winter, J. D., Koopman, S. J. & Hindrayanto, I. (2022). Joint Decomposition of Business and Financial Cycles: Evidence from Eight Advanced Economies*. Oxford Bulletin of Economics and Statistics, 84(1), 57-79. https://doi.org/10.1111/obes.12459

Blasques, F., van Brummelen, J., Koopman, S. J. & Lucas, A. (2022). Maximum likelihood estimation for score-driven models. Journal of Econometrics, 227(2), 325-346. https://doi.org/10.1016/j.jeconom.2021.06.003

de Bruijn, J. A., Daniell, J. E., Pomonis, A., Gunasekera, R., Macabuag, J., de Ruiter, M. C., Koopman, S. J., Bloemendaal, N., de Moel, H. & Aerts, J. C. J. H. (2022). Using rapid damage observations for Bayesian updating of hurricane vulnerability functions: A case study of Hurricane Dorian using social media. International Journal of Disaster Risk Reduction, 72, Article 102839. https://doi.org/10.1016/j.ijdrr.2022.102839

Blasques, F., Gorgi, P. & Koopman, S. J. (2021). Missing observations in observation-driven time series models. Journal of Econometrics, 221(2), 542-568. https://doi.org/10.1016/j.jeconom.2020.07.043

Bennedsen, M., Hillebrand, E. & Koopman, S. J. (2021). Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors. Energy Economics, 96, Article 105118. https://doi.org/10.1016/j.eneco.2021.105118

Li, M. & Koopman, S. J. (2021). Unobserved components with stochastic volatility: Simulation-based estimation and signal extraction. Journal of Applied Econometrics, 36(5), 614-627. https://doi.org/10.1002/jae.2831

Bennedsen, M., Hillebrand, E. & Koopman, S. J. (2020). A statistical model of the global carbon budget. Institut for Økonomi, Aarhus Universitet. CREATES Research Paper No. 2020-18

Li, M., Koopman, S. J., Lit, R. & Petrova, D. (2020). Long-term forecasting of El Niño events via dynamic factor simulations. Journal of Econometrics, 214(1), 46-66. https://doi.org/10.1016/j.jeconom.2019.05.004

Petrova, D., Ballester, J., Koopman, S. J. & Rodó, X. (2020). Multiyear statistical prediction of ENSO enhanced by the tropical Pacific observing system. Journal of Climate, 33(1), 163-174. https://doi.org/10.1175/JCLI-D-18-0877.1

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