Siem Jan Koopman | research | projects |

Research portals

• ORCID
  

• VU Research Portal
  

Working papers

• Continuous-time state-space methods for delta-O-18 and delta-C-13 by Mikkel Bennedsen, Eric Hillebrand, SJK and Kathrine By Larsen (2024).

• Estimating Breakpoints between Climate States in Paleoclimate Data by Mikkel Bennedsen, Eric Hillebrand, SJK and Kathrine By Larsen (2024).

• Asymmetric Stable Stochastic Volatility Models: Estimation, Filtering, and Forecasting by Francisco Blasques, SJK and Karim Moussa (2023).

• A New Approach to the CO2 Airborne Fraction: Enhancing Statistical Precision and Tackling Zero Emissions by Mikkel Bennedsen, Eric Hillebrand and SJK (2023). The code is here.

• Observation-Driven filters for Time-Series with Stochastic Trends and Mixed Causal Non-Causal Dynamics by Francisco Blasques, SJK and Gabriele Mingoli (2023).

• A Multilevel Factor Model for Economic Activity with Observation-Driven Dynamic Factors by Mariia Artimova, Francisco Blasques and SJK (2023).

• Extremum Monte Carlo Filters: Real-Time Signal Extraction via Simulation and Regression by Francisco Blasques, SJK and Karim Moussa (2023).

• Does trade integration imply growth in Latin America? Evidence from a dynamic spatial spillover model by Francisco Blasques, Paolo Gorgi, SJK and James Sampi (2023).

• Finding the European crime drop using a panel data model with stochastic trends by SJK and Ilka van de Werve (2022).

• Conditional score residuals and diagnostic analysis of serial dependence in time series models by Francisco Blasques, Paolo Gorgi and SJK (2021).

• An empirical assessment of the U.S. Phillips curve over time by Marente Vlekke, SJK and Martin Mellens (2021).

• Time-varying state correlations in state space models and their estimation via indirect inference by Caterina Schiavoni, SJK, Franz Palm, Stephan Smeekes and Jan van den Brakel (2021).

• Joint Modelling and Estimation of Global and Local Cross-Sectional Dependence in Large Panels by SJK, Julia Schaumburg and Quint Wiersma (2021).

• Forecasting in a changing world: from the great recession to the COVID-19 pandemic by Mariia Artemova, Francisco Blasques, SJK and Zhaokun Zhang (2021).

• Bayesian Risk Forecasting for Long Horizons by Agnieszka Borowska, Lennart Hoogerheide and SJK (2019).

Published articles

2024

1. Common and Idiosyncratic Conditional Volatility Factors: Theory and Empirical Evidence by F. Blasques, E. D'Innocenzo and S.J. Koopman, Econometric Reviews 2024, Volume 43, forthcoming. (Download Working Paper)

2. Vector Autoregressions with Dynamic Factor Coefficients and Conditionally Heteroskedastic Errors by P. Gorgi, S.J. Koopman and J. Schaumburg, Journal of Econometrics 2024, Volume 238, forthcoming. (Download Working Paper)

3. A robust Beveridge–Nelson decomposition using a score-driven approach with an application by F. Blasques, J. van Brummelen, P. Gorgi and S.J. Koopman, Economics Letters 2024, Volume 236, 111588. (Download Abstract + paper)

4. Observation-Driven Filtering of Time-Varying Parameters using Moment Conditions by D. Creal, S.J. Koopman, A. Lucas and M. Zamojski, Journal of Econometrics 2024, Volume 238(2), 105635. (Download Abstract + paper)

5. Maximum Likelihood Estimation for Non-Stationary Location Models with Mixture of Normal Distributions by F. Blasques, J. van Brummelen, P. Gorgi and S.J. Koopman, Journal of Econometrics 2024, Volume 238(1), 105575. (Download Abstract + paper)

   2023

6. Time-Varying Parameters in Econometrics: The editor’s foreword by F. Blasques, A.C. Harvey, S.J. Koopman and A. Lucas, Journal of Econometrics 2023, Volume 237(2B), Page 105439. (Download Article)

7. Estimation of final standings in football competitions with a premature ending: the case of COVID-19 by P. Gorgi, S.J. Koopman and R. Lit, AStA Advances in Statistical Analysis 2023, Volume 107(1-2), Page 233 - 250. (Download Abstract + paper)

8. Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects by P. Gorgi and S.J. Koopman, Journal of Econometrics 2023, Volume 237(2B), Page 105177. (Download Abstract + paper)

9. Matters Arising: On the evidence of a trend in the CO2 airborne fraction by M. Bennedsen, E. Hillebrand and S.J. Koopman, Nature, 2023, Volume 616, E1 - E3. (Download Article + code)

10. A Multivariate Dynamic Statistical Model of the Global Carbon Budget 1959--2020 by M. Bennedsen, E. Hillebrand and S.J. Koopman, Journal of the Royal Statistical Society Series A, 2023, Volume 186(1), Pages 20 - 42, with paper website here. (Download Abstract + paper)

    2022

11. Score-Driven Models: Methodology and Theory by M. Artemova, F. Blasques, J. van Brummelen and S.J. Koopman, Oxford Research Encyclopedia of Economics and Finance (Download Abstract + paper or Chapter 1 pdf)

12. Score-Driven Models: Methods and Applications by M. Artemova, F. Blasques, J. van Brummelen and S.J. Koopman, Oxford Research Encyclopedia of Economics and Finance (Download Abstract + paper or Chapter 2 pdf)

13. Maximum Likelihood Estimation for Score-Driven Models by F. Blasques, J. van Brummelen, S.J. Koopman and A. Lucas, Journal of Econometrics 2022, Volume 227, Pages 325 - 346. (Download Abstract + paper, Working Paper and Technical Appendix)

14. Joint decomposition of business and financial cycles: evidence from eight advanced economies, by J. de Winter, S.J. Koopman and I. Hindrayanto, Oxford Bulletin of Economics and Statistics 2022, Volume 84, Pages 57 - 79. (Download Abstract + paper)

15. Using rapid damage observations for Bayesian updating of hurricane vulnerability functions: A case study of Hurricane Dorian using social media by Jens A. de Bruijn, James E. Daniell, Antonios Pomonis, Rashmin Gunasekera, Joshua Macabuag, Marleen C. de Ruiter, S.J. Koopman, Nadia Bloemendaal, Hans de Moel and Jeroen C.J.H. Aerts, International Journal of Disaster Risk Reduction 2022, Volume 71, 102839. (Download Abstract + paper)

16. A Time-Varying Parameter Model for Local Explosions by F. Blasques, S.J. Koopman and M. Nientker, Journal of Econometrics 2022, Volume 227, Pages 65 - 84. (Download Abstract + paper)

    2021

17. Unobserved Components with Stochastic Volatility in U.S. Inflation: Estimation and Signal Extraction, by M. Li and S.J. Koopman, Journal of Applied Econometrics 2021, Volume 36, Pages 614 - 627. (Download Abstract + paper)

18. Dynamic Factor Models with Clustered Loadings: Forecasting Education Flows using Unemployment Data by F. Blasques, M. Heres Hoogerkamp, S.J. Koopman and I. van de Werve, International Journal of Forecasting 2021, Volume 37, Issue 4, Pages 1426 - 1441. (Download Abstract + paper)

19. Modeling, Forecasting and Nowcasting U.S. CO2 Emissions Using Many Macroeconomic Predictors, by M. Bennedsen, E. Hillebrand and S.J. Koopman, Energy Economics 2021, Volume 96, 105118. (Download Abstract + paper)

20. Missing Observations in Observation-Driven Time Series Models, by F. Blasques, P. Gorgi and S.J. Koopman, Journal of Econometrics 2021, Volume 221, Pages 542 - 568. (Download Abstract + paper)

    2020

21. Partially Censored Posterior for Robust and Efficient Risk Evaluation, by A. Borowska, L. Hoogerheide, S.J. Koopman and H.K. van Dijk, Journal of Econometrics 2020, Volume 217, Pages 335 - 355. (Download Abstract + paper)

22. Nonlinear Autoregressive Models with Optimality Properties, by F. Blasques, S.J. Koopman and A. Lucas, Econometric Reviews 2020, Volume 39, Issue 6, Pages 559 - 578. (Download Abstract + paper)

23. The Dynamic Factor Network Model with an Application to International Trade, by F. Brauning and S.J. Koopman, Journal of Econometrics 2020, Volume 216, Pages 494 - 515. (Download Abstract + paper)

24. Multiyear statistical prediction of ENSO enhanced by the Tropical Pacific Observing System, by D. Petrova, J. Ballester, S.J. Koopman and X. Rodo, Journal of Climate 2020, Volume 33, No. 1, Pages 163 - 174. (Download Abstract + paper)

25. Long Term Forecasting of El Nino Events via Dynamic Factor Simulations, by M. Li, S.J. Koopman, R. Lit and D. Petrova, Journal of Econometrics 2020, Volume 214, Pages 46 - 66. (Download Abstract + paper)

    2019

26. The analysis and forecasting of tennis matches using a high-dimensional dynamic model, by P. Gorgi, S.J. Koopman and R. Lit, Journal of the Royal Statistical Society Series A, 2019, Volume 182, Part 4, Pages 1393 - 1409. (Download Abstract + paper)

27. Sensitivity of large dengue epidemics in Ecuador to long-lead predictions of El Nino, by D. Petrova, R. Lowe, A. Stewart-Ibarra, J. Ballester, S.J. Koopman and X. Rodo, Climate Services 2019, Volume 15, August, 100096. (Download Abstract + paper)

28. Forecasting economic time series using score-driven dynamic models with mixed-data sampling, by P. Gorgi, S.J. Koopman and M. Li, International Journal of Forecasting 2019, Volume 35, Issue 4, Pages 1735 - 1747. (Download Abstract + paper)

29. Trend analysis of the airborne fraction and sink rate of anthropogenically released CO2, by M. Bennedsen, E. Hillebrand and S.J. Koopman, Biogeosciences 2019, Volume 16, Pages 3651 - 3663. (Download Abstract + paper)

30. Accelerating Score Driven Models, by F. Blasques, P. Gorgi and S.J. Koopman, Journal of Econometrics 2019, Volume 212, Pages 359 - 376. (Download Abstract + paper)

31. Forecasting football match results in national league competitions using score-driven time series models, by S.J. Koopman and R. Lit, International Journal of Forecasting 2019, Volume 35, Issue 2, Pages 797 - 809. (Download Abstract + paper)

32. Realized Wishart-GARCH: A Score-driven Multi-Asset Volatility Model, by P. Gorgi, P.R. Hansen, P. Janus and S.J. Koopman, Journal of Financial Econometrics 2019, Volume 17, Issue 1, Pages 1 - 32. (Download Abstract + paper)

33. Modified Efficient Importance Sampling for partially non-Gaussian State Space Models, by S.J. Koopman, R. Lit and T.M. Nguyen, Statistica Neerlandica 2019, Volume 73, Issue 1, Pages 44 - 62. (Download Full Article (open access))

    2018

34. Dynamic Discrete Copula Models for High Frequency Stock Price Changes, by S.J. Koopman, R. Lit, A. Lucas and A. Opschoor, Journal of Applied Econometrics 2018, Volume 33, Issue 7, Pages 966 - 985. (Download Full Article (open access))

35. Bayesian Dynamic Modeling of High-Frequency Integer Price Changes, by I. Barra, A. Borowska and S.J. Koopman, Journal of Financial Econometrics 2018, Volume 16, Issue 3, Pages 384 - 424. (Download Abstract + paper)

36. Feasible Invertibility Conditions and Maximum Likelihood Estimation for Observation-Driven Models, by F. Blasques, P. Gorgi, S.J. Koopman and O. Wintenberger, Electronic Journal of Statistics 2018, Volume 12, Pages 1019 - 1052. (Download Full Article (open access))

    2017

37. Intraday Stochastic Volatility in Discrete Price Changes: the Dynamic Skellam Model, by S.J. Koopman, R. Lit and A. Lucas, Journal of the American Statistical Association 2017, Volume 112 (520), 1490 - 1503. (Download Full Article (open access))

38. Joint Bayesian analysis of parameters and states in nonlinear non-Gaussian state space models, by I. Barra, L. Hoogerheide, S.J. Koopman and A. Lucas, Journal of Applied Econometrics, 2017, Volume 32 (5), 1003 - 1026. (Download Abstract + paper)

39. Empirical Bayes Methods for Dynamic Factor Models, by S.J. Koopman and G. Mesters, Review of Economics and Statistics, 2017, Volume 99 (3), 486 - 498. (Download Abstract + paper)

40. Time Varying Transition Probabilities for Markov Regime Switching Models, by M. Bazzi, F. Blasques, S.J. Koopman and A. Lucas, Journal of Time Series Analysis 2017, Volume 38, 458 - 478. (Download Abstract + paper)

41. Testing for Parameter Instability across Different Modeling Frameworks, by F. Calvori, D. Creal, S.J. Koopman and A. Lucas, Journal of Financial Econometrics 2017, Volume 15, 223 - 246. (Download Abstract + paper)

42. Global Credit Risk: World, Country and Industry Factors, by B. Schwaab, S.J. Koopman and A. Lucas, Journal of Applied Econometrics, 2017, Volume 32, 296 - 317. (Download Abstract + paper)

43. Improving the Long-Lead Predictability of El Nino Using a Novel Forecasting Scheme Based on a Dynamic Components Model, by D. Petrova, S.J. Koopman, J. Ballester and X. Rodo, Climate Dynamics 2017, Volume 48, Issue 3, Pages 1249 - 1276. (Download Abstract + paper)

    2016

44. Spillover Dynamics for Systemic Risk Measurement using Spatial Financial Time Series Models, by F. Blasques, S.J. Koopman, A. Lucas and J. Schaumburg, Journal of Econometrics 2016, Volume 195, Pages 211 - 223. (Download Abstract + paper)

45. The Information in Systemic Risk Rankings, by F. Nucera, B. Schwaab, S.J. Koopman and A. Lucas, Journal of Empirical Finance, 2016, Volume 38, Pages 461 - 475. (Download Abstract + paper)

46. Forecasting and nowcasting economic growth in the euro area using factor models, by I. Hindrayanto, S.J. Koopman and J. de Winter, International Journal of Forecasting 2016, Volume 32, Pages 1284 - 1305. (Download Abstract + paper)

47. Measuring Financial Cycles in a Model-Based Analysis: Empirical Evidence for the United States and the Euro Area, by Galati, I. Hindrayanto, S.J. Koopman and M. Vlekke, Economics Letters 2016, Volume 145, Pages 83 - 87. (Download Abstract + paper)

48. Weighted Maximum Likelihood for Dynamic Factor Analysis and Forecasting with Mixed Frequency Data, by F. Blasques, S.J. Koopman, M. Mallee and Z. Zhang, Journal of Econometrics 2016, Volume 193, Pages 405 - 417. (Download Abstract + paper)

49. In-Sample Confidence Bands and Out-of-Sample Forecast Bands for Time-Varying Parameters in Observation Driven Models (with discussion), by F. Blasques, K. Lasak, S.J. Koopman and A. Lucas, International Journal of Forecasting, 2016, Volume 32, Pages 875 - 887 and 893 - 894. (Download Abstract + paper)

50. Intervention Time Series Analysis of Crime Rates: The Case of Sentence Reform in Virginia, by S. Vujic, J.J.F. Commandeur and S.J. Koopman, Economic Modelling, 2016, Volume 57, Pages 311 - 323. (Download Abstract + paper)

51. Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models, by G. Mesters, S.J. Koopman and M. Ooms, Econometric Reviews 2016, Volume 35, Pages 659 - 687. (Download Abstract + paper)

52. Predicting time-varying parameters with parameter-driven and observation-driven models, by S. J. Koopman, A. Lucas and M. Scharth, Review of Economics and Statistics, 2016, Volume 98, Pages 97 - 110. (Download Abstract + paper)

    2015

53. Likelihood-based Dynamic Factor Analysis for Measurement and Forecasting, by B. Jungbacker and S.J. Koopman, Econometrics Journal, 2015, Volume 18, Pages C1 – C21. (Download Abstract + paper)

54. Information Theoretic Optimality of Observation Driven Time Series Models for Continuous Responses, by F. Blasques, S.J. Koopman and A. Lucas, Biometrika 2015, Volume 102, Pages 325 - 343. (Download Abstract + paper, with Correction 2017, Volume 104, forthcoming)

55. Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models, by S. J. Koopman, A. Lucas and M. Scharth, Journal of Business and Economic Statistics 2015, Volume 33, Pages 114 - 127. (Download Abstract + paper with Online Appendix)

56. A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League, by S.J. Koopman and R. Lit, Journal of the Royal Statistical Society Series A, 2015, Volume 178, Pages 167 - 186. (Download Abstract + paper)

    2014

57. Long Memory Dynamics for Multivariate Dependence under Heavy Tails, by P. Janus, S. J. Koopman and A. Lucas, Journal of Empirical Finance, 2014, Volume 29, Pages 187 - 206. (Download Abstract + paper)

58. Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk, by D. D. Creal, B. Schwaab, S. J. Koopman and A. Lucas, Review of Economics and Statistics, 2014, Volume 96, Pages 898 - 915. (Download Abstract + paper)

59. Stationarity and Ergodicity of Univariate Generalized Autoregressive Score Processes, by F. Blasques, S. J. Koopman and A. Lucas, Electronic Journal of Statistics 2014, Volume 8, Pages 1088 - 1112. (Download Abstract + paper)

60. Generalized Dynamic Panel Data Models with Random Effects for Cross-Section and Time, by G. Mesters and S.J. Koopman, Journal of Econometrics 2014, Volume 180, Pages 127 - 140. (Download Abstract + paper with Online Appendix here).

61. Nowcasting and forecasting global financial sector stress and credit market dislocation, by B. Schwaab, S. J. Koopman and A. Lucas, International Journal of Forecasting, 2014, Volume 30, Pages 741 - 758. (Download Abstract + paper)

62. Forecasting Macroeconomic Variables using Collapsed Dynamic Factor Analysis, by F. Brauning and S. J. Koopman, International Journal of Forecasting, 2014, Volume 30, Pages 572 - 584. (Download Abstract + paper)

63. Forecasting Interest Rates with Shifting Endpoints, by D. van Dijk, S. J. Koopman, M. van der Wel and J. H. Wright, Journal of Applied Econometrics, 2014, Volume 29, Pages 693 - 712. (Download Abstract + paper)

64. Smooth Dynamic Factor Analysis with Application to the U.S. Term Structure of Interest Rates, by B. Jungbacker, S. J. Koopman and M. van der Wel, Journal of Applied Econometrics, 2014, Volume 29, Pages 65 - 90. (Download Abstract + paper)

65. Long Memory with Stochastic Variance Model: a Recursive Analysis for U.S. Inflation, by C.S. Bos, S.J. Koopman and M. Ooms, Computational Statistics & Data Analysis, The Annals of Computational and Financial Econometrics, Second Issue, 2014, Volume 79, Pages 144 - 157. (Download Abstract + paper)

    2013

66. Forecasting the U.S. Term Structure of Interest Rates using a Macroeconomic Smooth Dynamic Factor Model, by S. J. Koopman and M. van der Wel, International Journal of Forecasting, 2013, Volume 29, Pages 676 - 694. (Download Abstract + paper)

67. Generalized Autoregressive Score Models with Applications, by D. D. Creal, S. J. Koopman and A. Lucas, Journal of Applied Econometrics, 2013, Volume 28, Pages 777 - 795. (Download Full Article (open access))

68. The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures, by S.J. Koopman and M. Scharth, Journal of Financial Econometrics, 2013, Volume 11, Pages 76 - 115. (Download Abstract + paper)

69. Modeling Trigonometric Seasonal Components for Monthly Economic Time Series, by I. Hindrayanto, J.A.D. Aston, S.J. Koopman and M. Ooms, Applied Economics, 2013, Volume 45, Pages 3024 - 3034. (Download Abstract + paper)

    2012

70. Dynamic factor models with macro, frailty, and industry effects for U.S. default counts: the credit crisis of 2008, by S. J. Koopman, A. Lucas and B. Schwaab, Journal of Business and Economic Statistics 2012, Volume 30, Pages 521 - 532. (Download Abstract + paper)

71. Economic Trends and Cycles in Crime: A Study for England and Wales, by S. Vujic, J.J.F. Commandeur and S.J. Koopman, Jahrbücher für Nationalökonomie und Statistik (Journal of Economics and Statistics), 2012, Volume 232, Pages 652 - 677. (Download Contents list)

72. Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling, by V. Dordonnat, S. J. Koopman and M. Ooms, Computational Statistics & Data Analysis, The Annals of Computational and Financial Econometrics, First Issue, 2012, Volume 56, Issue 11, Pages 3134 – 3152. (Download Abstract + paper).

73. Spot variance path estimation and its application to high frequency jump testing, by Charles S. Bos, P. Janus and S. J. Koopman, Journal of Financial Econometrics, 2012, Volume 10, Pages 354 - 389. (Download Abstract + paper)

    2011

74. A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations, by D. D. Creal, S. J. Koopman and A. Lucas, Journal of Business and Economic Statistics 2011, Volume 29, Pages 552 - 563. (Download Abstract + paper)

75. Maximum likelihood estimation for dynamic factor models with missing data, by B. Jungbacker, S.J. Koopman, and M. van der Wel, Journal of Economic Dynamics and Control 2011, Volume 35, 1358 - 1368. (Download Abstract + paper).

76. Modeling Frailty-correlated Defaults Using Many Macroeconomic Covariates, by S.J. Koopman, A. Lucas and B. Schwaab, Journal of Econometrics 2011, Volume 162, Pages 312 - 325. (Download Abstract + paper)

77. Statistical Software for State Space Methods, by J.J.F. Commandeur, S. J. Koopman and M. Ooms, Journal of Statistical Software 2011, Volume 41, Article 1. (Download Abstract + paper)

78. Kalman filtering and smoothing for model-based signal extraction that depend on time-varying spectra, by S. J. Koopman and S. Y. Wong, Journal of Forecasting 2011, Volume 30, Pages 147 - 167. (Download Abstract + paper)

    2010

79. Likelihood functions for state space models with diffuse initial conditions, by M.K. Francke, S. J. Koopman and A. de Vos, Journal of Time Series Analysis 2010, Volume 31, Pages 407 - 414. (Download Abstract + paper)

80. Analyzing the Term Structure of Interest Rates using the Dynamic Nelson-Siegel Model with Time-Varying Parameters, by S.J. Koopman, M.I.P. Mallee and M. van der Wel, Journal of Business and Economic Statistics 2010, Volume 28, Pages 329 - 343. (Download Abstract + paper)

81. Exact maximum likelihood estimation for non-stationary periodic time series models, by I. Hindrayanto, S. J. Koopman and M. Ooms, Computational Statistics & Data Analysis 2010, Volume 55, Pages 2641-2654. (Download Abstract + paper).

82. Intradaily smoothing splines for time-varying regression models of hourly electricity loads, by V. Dordonnat, S.J. Koopman and M. Ooms, The Journal of Energy Markets 2010, Volume 3, Pages 17-52. on-line version.

83. Extracting a robust U.S. business cycle using a time-varying multivariate model-based bandpass filter, by D. D. Creal, S. J. Koopman and E. Zivot, Journal of Applied Econometrics, 2010, Volume 25, Pages 695-719. (Download Abstract + paper)

84. Multivariate non-linear time series modeling of exposure and risk in road safety research, by F. Bijleveld, J. Commandeur, S.J. Koopman and K. van Montfort, Journal of the Royal Statistical Society Series C 2010, Volume 59, Pages 145-161. (Download Abstract + paper)

    2009

85. Unobserved components models in economics and finance: the role of the Kalman filter in time series econometrics, by A. C. Harvey and S. J. Koopman, IEEE Control Systems Magazine, 2009, Volume 29, Issue 6, Pages 71-81. (Download Abstract + paper)

86. Dynamic factors in state-space models for hourly electricity load signal decomposition and forecasting, by V. Dordonnat, S. J. Koopman and M. Ooms, IEEE Power & Energy Society 2009. (Download Abstract + paper)

87. Periodic Unobserved Cycles in Seasonal Time Series with an Application to U.S. Unemployment, by S.J. Koopman, M. Ooms and I. Hindrayanto, Oxford Bulletin of Economics and Statistics 2009, Volume 71, Pages 683-713. (Download Abstract + paper)

88. Seasonality with Trend and Cycle Interactions in Unobserved Components Models, by S. J. Koopman and K.M. Lee, Journal of the Royal Statistical Society Series C 2009, Volume 58, Pages 427-448. (Download Abstract + paper)

89. Testing the assumptions behind importance sampling, by S. J. Koopman, N. Shephard and D. D. Creal, Journal of Econometrics 2009, Volume 149, Pages 2-11. (Download Abstract + paper)

90. Credit Cycles and Macro Fundamentals , by S. J. Koopman, R. Kraussl, A. Lucas and A. Monteiro, Journal of Empirical Finance 2009, Volume 16, Pages 42–54. (Download Abstract + paper)

    2008

91. An Hourly Periodic State Space Model for Modelling French National Electricity Load , by V. Dordonnat, S. J. Koopman, M. Ooms, A. Dessertaine and J. Collet, International Journal of Forecasting 2008, Volume 24, Number 4, Pages 566-587. (Download Abstract + paper)

92. A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk , by S.J. Koopman and A. Lucas, Journal of Business and Economic Statistics 2008, Volume 26, Number 4, Pages 510-525. (Download Abstract + paper)

93. Estimating Systematic Continuous-time Trends in Recidivism using a Non-Gaussian Panel Data Model , by S.J. Koopman, A. Lucas, M. Ooms, K. van Montfort and V. van der Geest, Statistica Neerlandica 2008, Volume 62, Issue 1, Pages 104-130. (Download Abstract + paper)

94. Measuring Synchronisation and Convergence of Business Cycles , by S.J. Koopman and J. Valle e Azevedo, Oxford Bulletin of Economics and Statistics 2008, Volume 70, Issue 1, Pages 23-51. (Download Abstract + paper)

95. Model-based measurement of latent risk in time series with applications , by F. Bijleveld, J. Commandeur, P. Gould and S.J. Koopman, Journal of the Royal Statistical Society Series A 2008, Volume 171, Issue 1, Pages 265-277. (Download Abstract + paper)

96. The Multi-State Latent Factor Intensity Model for Credit Rating Transitions , by S.J. Koopman, A. Lucas and A. Monteiro, Journal of Econometrics 2008, Volume 142, Issue 1, Pages 399-424. (Download Abstract + paper)

    2007

97. Monte Carlo estimation for nonlinear non-Gaussian state space models , by B. Jungbacker and S.J. Koopman, Biometrika 2007, Volume 94, Pages 827-839. (Download Abstract + paper and Appendix)

98. Modelling Round-the-Clock Price Discovery for Cross-Listed Stocks using State Space Methods , by A. J. Menkveld, S.J. Koopman and A. Lucas, Journal of Business and Economic Statistics 2007, Volume 25, Number 2, April 2007, pp. 213-225. (Download Abstract + paper)

99. Periodic seasonal Reg-ARFIMA-GARCH models for daily electricity spot prices , by S.J. Koopman, M. Ooms and M. Angeles Carnero, Journal of the American Statistical Association 2007, Volume 102, Number 477, March 2007, pp. 16-27. (Download Abstract + paper)

    2006

100. Forecasting daily time series using periodic unobserved components time series models , by S.J. Koopman and M. Ooms, Computational Statistics & Data Analysis 2006, Volume 51, Issue 2, 15 November 2006, pp. 885-903. (Download Abstract + paper)

101. Monte Carlo likelihood estimation for three multivariate stochastic volatility models , by Borus Jungbacker and Siem Jan Koopman, Econometric Reviews 2006, Volume 25, Number 2-3 / 2006, pp. 385-408. (Download Abstract + paper)

102. A non-Gaussian generalisation of the Airline model for robust Seasonal Adjustment , by John Aston and S.J. Koopman, Journal of Forecasting 2006, Volume 25, Issue 5, pp. 325-349. (Download Abstract + paper)

103. Tracking the business cycle of the Euro area: a multivariate model-based band-pass filter , by Joao Valle e Azevedo, Siem Jan Koopman and Antonio Rua, Journal of Business and Economic Statistics, 2006, Volume 24, No. 3, July 2006, pp.278-290. (Download Abstract + paper)

     2005

104. Empirical Credit Cycles and Capital Buffer Formation , by S.J. Koopman, A. Lucas and P. Klaassen, Journal of Banking and Finance, 2005, Volume 29, Issue 12, pp 3159-3179. (Download Abstract + paper)

105. Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements , by S.J. Koopman, Borus Jungbacker and Eugenie Hol, Journal of Empirical Finance, 2005, Volume 12, Issue 3, Pages 445-475. (Download Abstract + paper)

106. Business and Default Cycles for Credit Risk , by S.J. Koopman and A. Lucas, Journal of Applied Econometrics, 2005, Volume 20, Issue 2, pp 311-323. (Download Abstract + paper)

     2004

107. Convergence in European GDP Series , by R.E. Luginbuhl and S.J. Koopman, Journal of Applied Econometrics, 2004, Volume 19, Issue 5, pp 611-636. (Download Abstract + paper)

108. Estimating stochastic volatility models: a comparison of two importance samplers , by K.M. Lee and S.J. Koopman, Studies in Nonlinear Dynamics and Econometrics, 2004, Volume 8, Issue 2, article 5. (Download Abstract + paper)

109. State space models with a common stochastic variance , by S.J. Koopman and C.S. Bos, Journal of Business and Economic Statistics, 2004, Volume 22, Issue 3, pp 346-357. (Download Abstract + paper)

     2003

110. Time Series Modelling of Daily Tax Revenues , by S.J. Koopman and M. Ooms, Statistica Neerlandica, 2003, Volume 57, Issue 4, pp 439-469. (Download Abstract + paper)

111. Computing Observation Weights for Signal Extraction and Filtering , by S.J. Koopman and A.C. Harvey, Journal of Economic Dynamic Control, 2003, Volume 27, Issue 7, pp 1317-1333. (Download Abstract + paper)

112. Filtering and smoothing of state vector for diffuse state space models , by S.J. Koopman and J. Durbin, Journal of Time Series Analysis, 2003, Volume 24, Issue 1, pp 85-98. (Download Abstract + paper)

     2002

113. Stochastic Volatility in Mean Model: Empirical evidence from international stock markets , by S.J. Koopman and E. Hol Uspensky, Journal of Applied Econometrics, 2002, Volume 17, Issue 6, pp 667-689. (Download Abstract + paper)

114. Constructing seasonally adjusted data with time-varying confidence intervals , by S.J. Koopman and P.H. Franses, Oxford Bulletin of Economics & Statistics, 2002, Volume 64, Issue 5, pp 509-526. (Download Abstract + paper)

115. A simple and efficient simulation smoother for state space time series analysis , by J. Durbin and S.J. Koopman, Biometrika, 2002, Volume 89, Issue 3, pp 603-616. (Download Abstract + paper)

     2001

116. Interaction between permanent and temporary shocks in production and employment , by S.J. Koopman and F.A.G. den Butter, Weltwirtschaftliches Archiv, 2001, 137, pp 273-296.

     2000

117. Signal Extraction and the Formulation of Unobserved Components Models , by A.C. Harvey and S.J. Koopman, Econometrics Journal, 2000, Volume 3, Issue 1, pp 84-107. (Download PDF document (292 kB))

118. Fast Filtering and smoothing for multivariate state space models , by S.J. Koopman and J. Durbin, Journal of Time Series Analysis, 2000, Volume 21, Issue 3, pp 281-296. (Download Abstract + paper)

119. Time Series Analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives , by S.J. Koopman and J. Durbin, Journal of Royal Statistical Society Series B, 2000, Volume 62, Issue 1, pp 3-56. (Download Abstract + paper)

     1990s

120. Statistical algorithms for models in state space form using SsfPack 2.2 , by S.J.Koopman, N.Shephard and J.A.Doornik, Econometrics Journal, 1999, Volume 2, p.113-166. (Download PDF document)
     Copyright for this article is held by the Royal Economic Society, but is made available on this site for personal use free of charge by permission of the Society. Note that SsfPack has its own website with software and data.

121. Bootstrap tests when parameters of nonstationary time series models lie on the boundary of the parameter space , with G.C. Franco and R.C. Souza, REBRAPE (Brazilian Journal of Probability and Statistics), 1999, Volume 13, pp 41-54.

122. Estimation of Stochastic Volatility Models via Monte Carlo Maximum Likelihood , with G. Sandmann, Journal of Econometrics, 1998, Volume 87, Issue 2, pp 271-301. (Download Abstract + paper)

123. Exact initial Kalman filtering and smoothing for non-stationary time series models , by S.J. Koopman, Journal of the American Statistical Association, 1997, Volume 92, Number 440, pp 1630-1638. (Download Abstract + paper)

124. Monte Carlo maximum likelihood estimation for non-Gaussian state space models , by J. Durbin and S.J. Koopman, Biometrika, 1997, Volume 84, pp 669-684. (Download Abstract + paper)

125. Detecting shocks: Outliers and Breaks in Time Series , by A.C. Atkinson, S.J. Koopman and N. Shephard, Journal of Econometrics, 1997, Volume 80, Issue 2, pp 387-422. (Download Abstract + paper)

126. The modelling and seasonal adjustment of weekly observations , by A.C. Harvey, S.J. Koopman and M. Riani, Journal of Business and Economic Statistics, 1997, Volume 15, Issue 3, pp 354-368. (Download Abstract + paper)

127. Structural time series models in medicine , with A.C. Harvey, Statistical Methods in Medical Research, 1996, Volume 5, pp 23-49.

128. Disturbance smoother for state space models, by S.J. Koopman, Biometrika, 1993, Volume 80, pp 117-126.

129. Forecasting hourly electricity demand using time-varying splines, with A.C. Harvey, Journal of the American Statistical Association, 1993, Volume 88, pp 1228-1236.

130. Exact score for time series models in state space form, with N. Shephard, Biometrika, 1992, Volume 79, pp 823-826.

131. Diagnostic checking of unobserved components time series models, with A.C. Harvey, Journal of Business and Economic Statistics, 1992, Volume 10, pp 377-389.

Publications (books)

1. Advances in Econometrics, Volume 35 "Dynamic Factor Models", 2016, edited by E. Hillebrand and S.J. Koopman, pp. 662, Bingley: Emerald Group.

2. Unobserved Components and Time Series Econometrics, 2015, edited by S.J. Koopman and N. Shephard, pp. 370, Oxford University Press.

3. Time Series Analysis by State Space Methods, Second Edition. 2012, with J. Durbin, pp. 368, Oxford University Press. You can order the book at OUP from UK or from USA

4. Statistical Algorithms for Models in State Space Form: SsfPack 3.0. 2008, with N. Shephard and J. A. Doornik, London, Timberlake Consultants.

5. STAMP 8 Structural Time Series Analyser, Modeller and Predictor. 2007, with A. C. Harvey, J. A. Doornik and N. Shephard, London, Timberlake Consultants.

6. An Introduction to State Space Time Series Analysis. 2007, with Jacques J.F. Commandeur, pp. 192, Oxford University Press.

7. STAMP 7 Structural Time Series Analyser, Modeller and Predictor. 2006, with A. C. Harvey, J. A. Doornik and N. Shephard, pp. 169, London, Timberlake Consultants.

8. State Space and Unobserved Component Models: Theory and Applications. Proceedings of a Conference in Honour of James Durbin., 2004, with A. C. Harvey and N. Shephard, pp. 393, Cambridge University Press.

9. Time Series Analysis by State Space Methods. 2001, with J. Durbin, pp. 253, Oxford University Press.

10. STAMP 6.0 Structural Time Series Analyser, Modeller and Predictor. 2000, with A. C. Harvey, J. A. Doornik and N. Shephard, pp. 221 (includes software), London, Timberlake Consultants.

11. STAMP 5.0 Structural Time Series Analyser, Modeller and Predictor. 1995, with A. C. Harvey, J. A. Doornik and N. Shephard, pp. 382 (includes software), London, Chapman and Hall.

12. STAMP 5.0 Tutorial Guide. 1995, with A. C. Harvey, J. A. Doornik and N. Shephard, pp. 91, London, Chapman and Hall.

Publications (contributions in books)

1. Continuous time state space modeling with an application to high-frequency road traffic data 2018, with Jacques J.F. Commandeur, Frits D. Bijleveld and Suncica Vujic, Chapter 17, in Kees van Montfort, Han Oud and Manual Voelkle (eds), Continuous Time Modeling in the Behavioral and Related Sciences, Berlin: Springer.

2. Model-based business cycle and financial cycle decomposition for Europe and the U.S. 2016, with Rutger Lit and Andre Lucas, Chapter 6, in M. Billio, L. Pelizzon and R. Savona (eds), Systemic Risk Tomography – Signals, Measurement and Transmission Channels, London: Elsevier-ISTE.

3. Time Series: State Space Models. 2015, with J.J.F. Commandeur, in James D. Wright (ed) and Kenneth Land (section ed), International Encyclopedia of the Social & Behavorial Sciences (Second Edition), New York: Elsevier.

4. Forecasting the Boat Race. 2015, with G. Mesters, Chapter 7, in S.J. Koopman and N. Shephard (eds), Unobserved Components and Time Series Econometrics (Festschrift Andrew C. Harvey), Oxford University Press.

5. Analysis of Historical Time Series with Messy Features: The Case of Commodity Prices in Babylonia. 2014, with L. Hoogerheide, in R.J. van der Spek, J. Luiten van Zanden and B. van Leeuwen (eds), A History of Market Performance: From Ancient Babylonia to the modern world, New York: Routledge, Chapter 3, p. 45-67.

6. A multivariate periodic unobserved components time series analysis for sectoral U.S. employment. 2012, with M. Ooms and I. Hindrayanto, in William R. Bell, Scott H. Holan and Tucker S. McElroy (eds), Economic Time Series: Modeling and Seasonality (Festschrift David F. Findley), London: Chapman and Hall/CRC Press, Chapter 1.

7. Forecasting economic time series using unobserved components time series models. 2011, with M. Ooms, in M.P. Clements and D.F. Hendry (eds), Oxford Handbook of Economic Forecasting, Oxford: Oxford University Press, Chapter 5, pp. 129-162.

8. State Space Methods for Latent Trajectory and Parameter Estimation by Maximum Likelihood. 2010, with J.J.F. Commandeur and K. van Montfort, in K. van Montfort, J.H.L. Oud, A. Satorra (eds), Longitudinal Research with Latent Variables, New York: Springer-Verlag, pp. 177-200.

9. Common Business and Housing Market Cyles in the Euro Area from a Multivariate Decomposition. 2010, with Laurent Ferrara, in J. de Bandt, T. Knetsch, J. Penalosa, F. Zollino (eds), Housing Markets in Europe, Berlin: Springer-Verlag, pp. 105-128.

10. Parameter Estimation and Practical Aspect of Modeling Stochastic Volatility. 2009, with B. Jungbacker, in T. Mikosch, J.-P. Kreiß, R.A. Davis, T.G. Andersen (eds), Handbook of Financial Time Series, New York: Springer-Verlag, pp. 313-44.

11. Model-based measurement of actual volatility in high-frequency data. 2005, with B. Jungbacker, in T. B. Fomby, D. Terrell (eds), Advances in Econometrics , Volume 20, New York: JAI Press. Download PDF document.

12. Trend-cycle decomposition models with smooth-transition parameters: evidence from US economic time series. 2005, with K.M. Lee and S.Y. Wong, in D. van Dijk, C. Milas and P.A. Rothman (eds), Nonlinear Time Series Analysis of Business Cycles, Elsevier.

13. State Space Modeling in Macroeconomics and Finance Using SsfPack in S+FinMetrics. 2004, with Eric Zivot and Jiahui Wang, in A.C.Harvey, S.J. Koopman and N. Shephard (eds), State Space and Unobserved Component Models: Theory and Applications, Cambridge University Press, pp. 284-335. [Download: article (PDF)]

14. State Space Methods. 2001, in A.H. El-Shaarawi and W.W. Piegorsch (eds), Encyclopedia of Environmetrics, Chichester: Wiley and Sons.

15. Messy Time Series. 1998, with A.C. Harvey and J. Penzer, in T.B. Fomby and R. Carter Hill (eds), Advances in Econometrics, Volume 13, New York: JAI Press.

16. Kalman filtering and smoothing. 1998, in P. Armitage and T. Colton (eds), The Encyclopedia of Biostatistics, Chichester: Wiley and Sons.

17. Structural time series models. 1998, with A.C. Harvey, in P. Armitage and T. Colton (eds), The Encyclopedia of Biostatistics, Chichester: Wiley and Sons.

18. Multivariate structural time series models. 1997, with A.C. Harvey, in C. Heij, H. Schumacher, B. Hanzon and C. Praagman (eds), Systematic dynamics in economic and financial models, Chichester: Wiley and Sons, pp. 269-298. [Download: article (PDF 2.373 kB) and references (PDF 212 kB)]

19. Outliers and switches in time series. 1994, with A.C. Atkinson and N. Shephard, in P. Mandle and M. Huskova (eds), Asymptotic Statistics, New York: Springer-Verlag.

20. Filtering, smoothing and estimation for time series models when the observations come from exponential family distributions. 1993, with J. Durbin, Bulletin of the International Statistical Institute, Book 1.

21. Cross-validation techniques for the analysis of covariance structures. 1988, with J.G. de Gooijer, in M. Jansen and W. van Schuur (eds), The many faces of multivariate analysis, Groningen: RION.

Other publications

1. Ajax zou normaliter ook bij het uitspelen van vorig seizoen kampioen geworden zijn (in Dutch), by P. Gorgi, S.J. Koopman and R. Lit, Economisch Statistische Berichten - ESB 2020, Blog 3 December.

2. Long-lead El Nino forecast information to support public health decision making: application to dengue epidemics in Ecuador, by D. Petrova, R. Lowe, A. Stewart-Ibarra, J. Ballester, S.J. Koopman and X. Rodo, The American Journal of Tropical Medicine and Hygiene 2016, Volume 95(5) Supplement, Entry 221, Page 70.

3. Durbin, James [Jim] (1923-2012) 2015, Entry in Oxford Dictionary of National Biography, Oxford University Press.

4. Obituary James Durbin, FBA, 1923–2012, Journal of the Royal Statistical Society Series A 2012, Volume 175, Issue 4, Pages 1060-1064. (Download Obituary)

5. Extending the Dynamic Nelson-Siegel Yield Curve Model, by S.J. Koopman and M. van der Wel, Medium for Econometric Applications, 2010, Volume 18, Issue 2.

6. Discussion of Exponentionally weighted methods for forecasting intraday time series with multiple seasonal cycles by J. W. Taylor., by S.J. Koopman and M. Ooms, International Journal of Forecasting, 2010, Volume 26, Issue 4, pp 647-651. (Download Paper)

7. Discussion of Particle Markov chain Monte Carlo methods by C. Andrieu, A. Doucet and R. Holenstein, by D.D. Creal and S.J. Koopman, Journal of Royal Statistical Society Series B, 2010, Volume 72, Issue 3, pp 320. (Download Paper)

8. Discussion of Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations by H. Rue, S. Martino and N. Chopin, by B. Jungbacker and S.J. Koopman, Journal of Royal Statistical Society Series B, 2009, Volume 71, Issue 2, pp 371-372. (Download Paper)

9. State-space fever!, 2007. Tinbergen Institute Magazine Fall, p. 8-11.

10. Preface in Computational Statistics & Data Analysis, by A. Amendola, C. Francq and S.J. Koopman, 2006, Computational Statistics & Data Analysis, special issue Nonlinear Modelling and Financial Econometrics, p. 1-3.

11. Toward X-13?, 2003, by Brian C. Monsell, John A.D. Aston and Siem Jan Koopman. U.S. Census Bureau. Download paper.

12. Periodic Structural Time Series Models: Estimation and Forecasting with Application, 2002, by S. J. Koopman and M. Ooms. Proceeding of the 3rd International Symposium on Frontiers of Time Series Modeling: Modeling Seasonality and Periodicity, Institute of Statistical Mathematics, Tokyo, Japan, p 151-172.

13. Seasonal Adjustment, 2002. Tinbergen Institute Magazine 5, p. 7-11.

14. Discussion of MCMC based inference by R. Paap. 2002, Statistica Neerlandica, 55, p. 34-40.

15. Inaugural speech (in Dutch) Met het Kalman filter vooruit, 2000, Kwantitatieve Methoden, 64, p.117-145.

16. Estimation of exponential family time series models using importance sampling, 2000, by S. J. Koopman. Proceedings of the 1st International Symposium on Frontiers of Time Series Modeling: Modeling Seasonality and Periodicity, Institute of Statistical Mathematics, Tokyo, Japan, p 46-57.

17. Review of Applied Bayesian Forecasting and Time Series Analysis by Andy Pole, Mike West and Jeff Harrison. 1997, Journal of Time Series Analysis, 18, p.533-534.