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Home | People | Siem Jan Koopman
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Siem Jan Koopman

Research Fellow

University
Vrije Universiteit Amsterdam
Researchgroup
Econometrics
Interests
econometric methodology, financial econometrics, time series econometrics

Biography

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.

Key publications

List of publications

Koopman, Siem Jan and Rutger Lit. 2019. Forecasting football match results in national league competitions using score-driven time series models. International Journal of Forecasting, 0169-2070

Paolo Gorgi and Koopman, Siem Jan and Mengheng Li. 2019. Forecasting economic time series using score-driven dynamic models with mixed-data sampling. International Journal of Forecasting, 0169-2070

Koopman, Siem Jan and Rutger Lit and André Lucas and Anne Opschoor. 2018. Dynamic discrete copula models for high-frequency stock price changes. Journal of Applied Econometrics, 33, 966--985, 0883-7252

István Barra and Agnieszka Borowska and Koopman, Siem Jan. 2018. Bayesian dynamic modeling of high-frequency integer price changes. Journal of Financial Econometrics, 16, 384--424, 1479-8409

I. Barra and L.F. Hoogerheide and S.J. Koopman and A. Lucas. 2017. Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models. Journal of Applied Econometrics, 32, 1003--1026, 0883-7252

B. Schwaab and S.J. Koopman and A. Lucas. 2017. Global Credit Risk: World, Country and Industry Factors. Journal of Applied Econometrics, 32, 296--317, 0883-7252

S.J. Koopman and G. Mesters. 2017. Empirical Bayes Methods for Dynamic Factor Models. Review of Economics and Statistics, 99, 486--498, 0034-6535

S.J. Koopman and R. Lit and A. Lucas. 2017. Intraday Stochastic Volatility in Discrete Price Changes: the Dynamic Skellam Model. Journal of the American Statistical Association, 112, 1490--1503, 0162-1459

Francesco Calvori and Drew Creal and Koopman, Siem Jan and André Lucas. 2017. Testing for parameter instability across different modeling frameworks. Journal of Financial Econometrics, 15, 223--246, 1479-8409

F. Blasques and S.J. Koopman and A. Lucas and J. Schaumburg. 2016. Spillover dynamics for systemic risk measurement using spatial financial time series models. Journal of Econometrics, 195, 211--223, 0304-4076

Blasques Albergaria Amaral, F. and S.J. Koopman and M.I.P. Mallee and Z. Zhang. 2016. Weighted Maximum Likelihood for Dynamic Factor Analysis and Forecasting with Mixed Frequency Data. Journal of Econometrics, 193, 405--417, 0304-4076

S.J. Koopman and A. Lucas and M. Scharth. 2016. Predicting time-varying parameters with parameter-driven and observation-driven models. Review of Economics and Statistics, 98, 97--110, 0034-6535

F. Blasques and S.J. Koopman and K.A. Lasak and A. Lucas. 2016. Rejoinder to the discussion 'In-Sample Confidence Bands and Out-of-Sample Forecast Bands for Time-Varying Parameters in Observation-Driven Models'. International Journal of Forecasting, 32, 893--894, 0169-2070

S. Vujic and J.J.F. Commandeur and S.J. Koopman. 2016. Intervention time series analysis of crime rates: The case of sentence reform in Virginia. Economic Modelling, 57, 311--323, 0264-9993

F. Blasques and S.J. Koopman and K.A. Lasak and A. Lucas. 2016. In-Sample Confidence Bands and Out-of-Sample Forecast Bands for Time-Varying Parameters in Observation Driven Models. International Journal of Forecasting, 32, 875--887, 0169-2070

G. Mesters and S.J. Koopman and M. Ooms. 2016. Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models. Econometric Reviews, 35, 659--687, 0747-4938

A.I.W. Hindrayanto and S.J. Koopman and de Winter, J.. 2016. Forecasting and nowcasting economic growth in the euro area using factor models. International Journal of Forecasting, 32, 1284--1305, 0169-2070

F. Nucera and B. Schwaab and S.J. Koopman and A. Lucas. 2016. The Information in Systemic Risk Rankings. Journal of Empirical Finance, 38A, 461--475, 0927-5398

E.B.G. Galati and A.I.W. Hindrayanto and S.J. Koopman and M. Vlekke. 2016. Measuring Financial Cycles in a Model-Based Analysis: Empirical Evidence for the United States and the Euro Area. Economics Letters, 145, 83--87, 0165-1765

S.J. Koopman and A. Lucas and M. Scharth. 2015. Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models. Journal of Business and Economic Statistics, 33, 114--127, 0735-0015

B.M.J.P. Jungbacker and S.J. Koopman. 2015. Likelihood-based Dynamic Factor Analysis for Measurement and Forecasting. Econometrics Journal, 18, C1--C21, 1368-4221

Dijk, D. van and S.J. Koopman and van der Wel, M. and J.H. Wright. 2014. Forecasting Interest Rates with Shifting Endpoints. Journal of Applied Econometrics, 29, 693--712, 0883-7252

D.D. Creal and B. Schwaab and S.J. Koopman and A. Lucas. 2014. Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk. Review of Economics and Statistics, 96, 898--915, 0034-6535

G. Mesters and S.J. Koopman. 2014. Generalized Dynamic Panel Data Models with Random Effects for Cross-Section and Time. Journal of Econometrics, 180, 127--140, 0304-4076

B.M.J.P. Jungbacker and S.J. Koopman and van der Wel, M.. 2014. Smooth Dynamic Factor Analysis with Application to the U.S. Term Structure of Interest Rates. Journal of Applied Econometrics, 29, 65--90, 0883-7252

S.J. Koopman and A. Lucas and B. Schwaab. 2014. Nowcasting and forecasting global financial sector stress and credit market dislocation. International Journal of Forecasting, 30, 741--758, 0169-2070

P. Janus and S.J. Koopman and A. Lucas. 2014. Long memory dynamics for multivariate dependence under heavy tails. Journal of Empirical Finance, 29, 187--206, 0927-5398

F.U. Brauning and S.J. Koopman. 2014. Forecasting Macroeconomic Variables using Collapsed Dynamic Factor Analysis. International Journal of Forecasting, 30, 572--584, 0169-2070

D.D. Creal and S.J. Koopman and A. Lucas. 2013. General Autoregressive Score Models with Applications. Journal of Applied Econometrics, 28, 777--795, 0883-7252

S.J. Koopman and van der Wel, M.. 2013. Forecasting the U.S. Term Structure of Interest Rates using a Macroeconomic Smooth Dynamic Factor Model. International Journal of Forecasting, 29, 676--694, 0169-2070

S.J. Koopman and M. Scharth. 2013. The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures. Journal of Financial Econometrics, 11, 76--115, 1479-8409

A.I.W. Hindrayanto and J.A.D. Aston and S.J. Koopman and M. Ooms. 2013. Modeling trigonometric seasonal components for monthly economic time series. Applied Economics, 45, 3024--3034, 0003-6846

S.J. Koopman and A. Lucas and B. Schwaab. 2012. Dynamic Factor Models With Macro, Frailty and Industry Effects for U.S. Default Counts: The Credit Crisis of 2008. Journal of Business and Economic Statistics, 30, 521--532, 0735-0015

C.S. Bos and P. Janus and S.J. Koopman. 2012. Spot Variance Path Estimation and its Application to High Frequency Jump Testing. Journal of Financial Econometrics, 10, 354--389, 1479-8409

S.J. Koopman and A. Lucas and B. Schwaab. 2011. Modeling frailty correlated defaults using many macroeconomic covariates. Journal of Econometrics, 162, 312--325, 0304-4076

D.D. Creal and S.J. Koopman and A. Lucas. 2011. A dynamic multivariate heavy-tailed model for time-varying volatilities and correlations. Journal of Business and Economic Statistics, 29, 552--563, 0735-0015

B.M.J.P. Jungbacker and S.J. Koopman and van der Wel, M.. 2011. Maximum likelihood estimation for dynamic factor models with missing data. Journal of Economic Dynamics and Control, 35, 1358--1368, 0165-1889

S.J. Koopman and S.Y. Wong. 2011. Kalman filtering and smoothing for model-based signal extraction that depend on time-varying spectra. Journal of Forecasting, 30, 147--167, 0277-6693

S.J. Koopman and D.D. Creal. 2010. Extracting a robust U.S. business cycle using a time-varying multivariate model-based bandpass filter. Journal of Applied Econometrics, 25, 695--719, 0883-7252

S.J. Koopman and M.I.P. Mallee and van der Wel, M.. 2010. Analyzing the term structure of interest rates using the dynamic Nelson-Siegel model with time-varying parameters. Journal of Business and Economic Statistics, 28, 329--343, 0735-0015

S.J. Koopman and M. Ooms. 2010. Discussion of `Exponentionally Weighted Methods for Forecasting Intraday Time Series with Multiple Seasonal Cycles -- James W. Taylor' [Review of: Exponentionally Weighted Methods for Forecasting Intraday Time Series with Multiple Seasonal Cycles]. International Journal of Forecasting, 26, 627--651, 0169-2070

S.J. Koopman and N. Shephard and D.D. Creal. 2009. Testing the assumptions behind importance sampling. Journal of Econometrics, 149, 2--11, 0304-4076

S.J. Koopman and R.G.W. Kraeussl and A. Lucas and A. Monteiro. 2009. Credit cycles and macro fundamentals. Journal of Empirical Finance, 16, 42--54, 0927-5398

S.J. Koopman and M. Ooms and A.I.W. Hindrayanto. 2009. Periodic unobserved cycles in seasonal time series with an application to U.S. unemployment. Oxford Bulletin of Economics and Statistics, 71, 683--713, 0305-9049

S.J. Koopman and A. Lucas and A. Monteiro. 2008. The Multi-state Latent Factor Intensity Model for Credit Rating Transitions. Journal of Econometrics, 142, 399--424, 0304-4076

S.J. Koopman and A. Lucas. 2008. A Non-Gaussian Panel Time series Model for Estimating and Decomposing Default Risk. Journal of Business and Economic Statistics, 26, 510--525, 0735-0015

S.J. Koopman and V. Dordonnat and M. Ooms. 2008. An Hourly Periodic State Space Model for Modelling French National Electricity Load. International Journal of Forecasting, 24, 566--587, 0169-2070

S.J. Koopman and Valle a Azevedo, J.. 2008. Measuring Synchronisation and Convergence of Business Cycles in Eurozone, UK and US. Oxford Bulletin of Economics and Statistics, 70, 23--51, 0305-9049

M. Ooms and S.J. Koopman and A.M. Carnero. 2007. Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices. Journal of the American Statistical Association, 102, 16--27, 0162-1459

A.J. Menkveld and S.J. Koopman and A. Lucas. 2007. Modelling Round-the-Clock Price Discovery for Cross-Listed Stocks using State Space Methods. Journal of Business and Economic Statistics, 25, 213--255, 0735-0015

J.M. Azevedo and S.J. Koopman and A. Rua. 2006. Tracking the business cycle of the Euro area: A multivariate model-based band-pass filter. Journal of Business and Economic Statistics, 24, 278--290, 0735-0015

B.M.J.P. Jungbacker and S.J. Koopman. 2006. Monte Carlo likelihood estimation for three multivariate stochastic volatility models. Econometric Reviews, 25, 385--408, 0747-4938

J. Aston and S.J. Koopman. 2006. A non-Gaussian generalisation of the Airline model for robust Seasonal Adjustment. Journal of Forecasting, 25, 325--349, 0277-6693

S.J. Koopman and A. Lucas. 2005. Business and Default Cycles for Credit Risk. Journal of Applied Econometrics, 20, 311--323, 0883-7252

S.J. Koopman and B.M.J.P. Jungbacker and E. Hol. 2005. Forecasting daily variability of the S\&P 100 stock index using historical, realised and implied volatility measurements. Journal of Empirical Finance, 12, 445--475, 0927-5398

S.J. Koopman and A. Lucas and P. Klaassen. 2005. Empirical Credit Cycles and Capital Buffer Formation. Journal of Banking and Finance, 29, 3159--3179, 0378-4266

S.J. Koopman and R.E. Luginbuhl. 2004. Convergence in European GDP Series. Journal of Applied Econometrics, 19, 611--636, 0883-7252

S.J. Koopman and C.S. Bos. 2004. State space models with a common stochastic variance. Journal of Business and Economic Statistics, 22, 346--357, 0735-0015

S.J. Koopman and K.M. Lee. 2004. Estimating stochastic volatility models: a comparison of two importance samplers. Studies in Nonlinear Dynamics and Econometrics, 8, 1--22, 1081-1826

S.J. Koopman and A.C. Harvey. 2003. Computing Observation Weights for Signal Extraction and Filtering. Journal of Economic Dynamics and Control, 27, 1317--1333, 0165-1889

S.J. Koopman and Hol Uspensky, E.. 2002. The Stochastic Volatility in Mean Model: Empirical evidence from international stock markets. Journal of Applied Econometrics, 17, 667--689, 0883-7252

S.J. Koopman and P.H. Franses. 2002. Constructing seasonally adjusted data with time-varying confidence intervals. Oxford Bulletin of Economics and Statistics, 64, 509--526, 0305-9049

A.C. Harvey and S.J. Koopman. 2000. Signal Extraction and the Formulation of Unobserved Components Models. Econometrics Journal, 3, 84--107, 1368-4221

S.J. Koopman and N. Shephard and J.A. Doornik. 1999. Statistical algorithms for models in state space using SsPack 2.2. Journal of Econometrics, 113--166, 0304-4076