Siem Jan Koopman
Key publications
List of publications
Blasques, F., van Brummelen, J., Gorgi, P. and Koopman, S.J. (2024). Maximum Likelihood Estimation for Non-Stationary Location Models with Mixture of Normal Distributions Journal of Econometrics, 238(1):1--22.
Blasques, F., van Brummelen, J., Gorgi, P. and Koopman, S.J. (2024). A robust Beveridge–Nelson decomposition using a score-driven approach with an application Economics Letters, 236:1--5.
Blasques, F., Harvey, A.C., Koopman, S.J. and Lucas, A. (2023). Time-Varying Parameters in Econometrics: The editor's foreword Journal of Econometrics, 237(2):.
Gorgi, P. and Koopman, S.J. (2023). Beta observation-driven models with exogenous regressors: A joint analysis of realized correlation and leverage effects Journal of Econometrics, 237(2):1--21.
Winter, J.D., Koopman, S.J. and 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.
Blasques, F., van Brummelen, J., Koopman, S.J. and Lucas, A. (2022). Maximum likelihood estimation for score-driven models Journal of Econometrics, 227(2):325--346.
Blasques, F., Koopman, S.J. and Nientker, M. (2022). A time-varying parameter model for local explosions Journal of Econometrics, 227(1):65--84.
Bennedsen, M., Hillebrand, E. and Koopman, S.J. (2021). Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors Energy Economics, 96:1--17.
van de Werve, I., Blasques, F., Koopman, S.J. and Heres Hoogerkamp, M. (2021). Dynamic factor models with clustered loadings: Forecasting education flows using unemployment data International Journal of Forecasting, 37(4):1426--1441.
Li, M. and Koopman, S. (2021). Unobserved components with stochastic volatility: Simulation-based estimation and signal extraction Journal of Applied Econometrics, 36(5):614--627.
Blasques, F., Gorgi, P. and Koopman, S.J. (2021). Missing observations in observation-driven time series models Journal of Econometrics, 221(2):542--568.
Li, M., Koopman, S.J., Lit, R. and Petrova, D. (2020). Long-term forecasting of El Niño events via dynamic factor simulations Journal of Econometrics, 214(1):46--66.
Blasques, F., Koopman, S.J. and Lucas, A. (2020). Nonlinear autoregressive models with optimality properties Econometric Reviews, 39(6):559--578.
Bräuning, F. and Koopman, S.J. (2020). The dynamic factor network model with an application to international trade Journal of Econometrics, 216(2):494--515.
Borowska, A., Hoogerheide, L., Koopman, S.J. and van Dijk, HermanK. (2020). Partially censored posterior for robust and efficient risk evaluation Journal of Econometrics, 217(2):335--355.
Koopman, S.J. and Lit, R. (2019). Forecasting football match results in national league competitions using score-driven time series models International Journal of Forecasting, 35(2):797--809.
Blasques, F., Gorgi, P. and Koopman, S.J. (2019). Accelerating score-driven time series models Journal of Econometrics, 212(2):359--376.
Gorgi, P., Koopman, S.J. and Li, M. (2019). Forecasting economic time series using score-driven dynamic models with mixed-data sampling International Journal of Forecasting, 35(4):1735--1747.
Barra, I., Borowska, A. and Koopman, S.J. (2018). Bayesian dynamic modeling of high-frequency integer price changes Journal of Financial Econometrics, 16(3):384--424.
Koopman, S.J., Lit, R., Lucas, A. and Opschoor, A. (2018). Dynamic discrete copula models for high-frequency stock price changes Journal of Applied Econometrics, 33(7):966--985.
Barra, I., Hoogerheide, L., Koopman, S. and Lucas, A. (2017). Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models Journal of Applied Econometrics, 32(5):1003--1026.
Calvori, F., Creal, D., Koopman, S.J. and Lucas, A. (2017). Testing for parameter instability across different modeling frameworks Journal of Financial Econometrics, 15(2):223--246.
Schwaab, B., Koopman, S. and Lucas, A. (2017). Global Credit Risk: World, Country and Industry Factors Journal of Applied Econometrics, 32(2):296--317.
Koopman, S. and Mesters, G. (2017). Empirical Bayes Methods for Dynamic Factor Models Review of Economics and Statistics, 99(3):486--498.
Koopman, S., Lit, R. and Lucas, A. (2017). Intraday Stochastic Volatility in Discrete Price Changes: the Dynamic Skellam Model Journal of the American Statistical Association, 112(520):1490--1503.
Blasques, F., Koopman, S., Lasak, K. and Lucas, A. (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(3):893--894.
Blasques, F., Koopman, S., Lucas, A. and Schaumburg, J. (2016). Spillover dynamics for systemic risk measurement using spatial financial time series models Journal of Econometrics, 195(2):211--223.
Vujic, S., Commandeur, J. and Koopman, S. (2016). Intervention time series analysis of crime rates: The case of sentence reform in Virginia Economic Modelling, 57:311--323.
Blasques Albergaria Amaral, F., Koopman, S., Mallee, M. and Zhang, Z. (2016). Weighted Maximum Likelihood for Dynamic Factor Analysis and Forecasting with Mixed Frequency Data Journal of Econometrics, 193(2):405--417.
Blasques, F., Koopman, S., Lasak, K. and Lucas, A. (2016). In-Sample Confidence Bands and Out-of-Sample Forecast Bands for Time-Varying Parameters in Observation Driven Models International Journal of Forecasting, 32(3):875--887.
Mesters, G., Koopman, S. and Ooms, M. (2016). Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models Econometric Reviews, 35(4):659--687.
Hindrayanto, A., Koopman, S. and de Winter, J. (2016). Forecasting and nowcasting economic growth in the euro area using factor models International Journal of Forecasting, 32(4):1284--1305.
Nucera, F., Schwaab, B., Koopman, S. and Lucas, A. (2016). The Information in Systemic Risk Rankings Journal of Empirical Finance, 38A(September):461--475.
Koopman, S., Lucas, A. and Scharth, M. (2016). Predicting time-varying parameters with parameter-driven and observation-driven models Review of Economics and Statistics, 98(1):97--110.
Galati, E., Hindrayanto, A., Koopman, S. and Vlekke, M. (2016). Measuring Financial Cycles in a Model-Based Analysis: Empirical Evidence for the United States and the Euro Area Economics Letters, 145:83--87.
Jungbacker, B. and Koopman, S. (2015). Likelihood-based Dynamic Factor Analysis for Measurement and Forecasting Econometrics Journal, 18(2):C1--C21.
Koopman, S., Lucas, A. and Scharth, M. (2015). Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models Journal of Business and Economic Statistics, 33(1):114--127.
Blasques, F., Koopman, S. and Lucas, A. (2015). Information Theoretic Optimality of Observation Driven Time Series Models Biometrika, 102(2):325--343.
Koopman, S., Lucas, A. and Schwaab, B. (2014). Nowcasting and forecasting global financial sector stress and credit market dislocation International Journal of Forecasting, 30(3):741--758.
Janus, P., Koopman, S. and Lucas, A. (2014). Long memory dynamics for multivariate dependence under heavy tails Journal of Empirical Finance, 29(December):187--206.
Creal, D., Schwaab, B., Koopman, S. and Lucas, A. (2014). Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk Review of Economics and Statistics, 96(5):898--915.
Mesters, G. and Koopman, S. (2014). Generalized Dynamic Panel Data Models with Random Effects for Cross-Section and Time Journal of Econometrics, 180(2):127--140.
Jungbacker, B., Koopman, S. 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(1):65--90.
Brauning, F. and Koopman, S. (2014). Forecasting Macroeconomic Variables using Collapsed Dynamic Factor Analysis International Journal of Forecasting, 30(3):572--584.
Dijk, D.van, Koopman, S., van der Wel, M. and Wright, J. (2014). Forecasting Interest Rates with Shifting Endpoints Journal of Applied Econometrics, 29(5):693--712.
Koopman, S. 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(4):676--694.
Koopman, S. and Scharth, M. (2013). The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures Journal of Financial Econometrics, 11(1):76--115.
Creal, D., Koopman, S. and Lucas, A. (2013). General Autoregressive Score Models with Applications Journal of Applied Econometrics, 28(5):777--795.
Hindrayanto, A., Aston, J., Koopman, S. and Ooms, M. (2013). Modeling trigonometric seasonal components for monthly economic time series Applied Economics, 45(21):3024--3034.
Bos, C., Janus, P. and Koopman, S. (2012). Spot Variance Path Estimation and its Application to High Frequency Jump Testing Journal of Financial Econometrics, 10(2):354--389.
Koopman, S., Lucas, A. and Schwaab, B. (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(4):521--532.
Koopman, S., Lucas, A. and Schwaab, B. (2011). Modeling frailty correlated defaults using many macroeconomic covariates Journal of Econometrics, 162(2):312--325.
Creal, D., Koopman, S. and Lucas, A. (2011). A dynamic multivariate heavy-tailed model for time-varying volatilities and correlations Journal of Business and Economic Statistics, 29(4):552--563.
Jungbacker, B., Koopman, S. and van der Wel, M. (2011). Maximum likelihood estimation for dynamic factor models with missing data Journal of Economic Dynamics and Control, 35(8):1358--1368.
Koopman, S. and Wong, S. (2011). Kalman filtering and smoothing for model-based signal extraction that depend on time-varying spectra Journal of Forecasting, 30(1):147--167.
Koopman, S. and Ooms, M. (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.
Koopman, S. and Creal, D. (2010). Extracting a robust U.S. business cycle using a time-varying multivariate model-based bandpass filter Journal of Applied Econometrics, 25:695--719.
Koopman, S., Mallee, M. 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.
Koopman, S. (2010). Discussion of `Particle Markov chain Monte Carlo methods – C. Andrieu, A. Doucet and R. Holenstein{\textquoteright} [Review of: Particle Markov chain Monte Carlo methods] Journal of the Royal Statistical Society. Series B. Statistical Methodology, 72:269--342.
Koopman, S., Shephard, N. and Creal, D. (2009). Testing the assumptions behind importance sampling Journal of Econometrics, 149:2--11.
Koopman, S., Kraeussl, R., Lucas, A. and Monteiro, A. (2009). Credit cycles and macro fundamentals Journal of Empirical Finance, 16:42--54.
Koopman, S., Ooms, M. and Hindrayanto, A. (2009). Periodic unobserved cycles in seasonal time series with an application to U.S. unemployment Oxford Bulletin of Economics and Statistics, 71:683--713.
Koopman, S., Lucas, A. and Monteiro, A. (2008). The Multi-state Latent Factor Intensity Model for Credit Rating Transitions Journal of Econometrics, 142:399--424.
Koopman, S., Dordonnat, V. and Ooms, M. (2008). An Hourly Periodic State Space Model for Modelling French National Electricity Load International Journal of Forecasting, 24:566--587.
Koopman, S. 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(1):23--51.
Koopman, S. and Lucas, A. (2008). A Non-Gaussian Panel Time series Model for Estimating and Decomposing Default Risk Journal of Business and Economic Statistics, 26(4):510--525.
Ooms, M., Koopman, S. and Carnero, A. (2007). Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices Journal of the American Statistical Association, 102(477):16--27.
Jungbacker, B. and Koopman, S. (2007). Monte Carlo estimation for nonlinear non-Gaussian state space models Biometrika, 94:827--839.
Menkveld, A., Koopman, S. and Lucas, A. (2007). Modelling Round-the-Clock Price Discovery for Cross-Listed Stocks using State Space Methods Journal of Business and Economic Statistics, 25(2):213--255.
Jungbacker, B. and Koopman, S. (2006). Monte Carlo likelihood estimation for three multivariate stochastic volatility models Econometric Reviews, 25(2-3):385--408.
Azevedo, J., Koopman, S. and Rua, A. (2006). Tracking the business cycle of the Euro area: A multivariate model-based band-pass filter Journal of Business and Economic Statistics, 24(3):278--290.
Aston, J. and Koopman, S. (2006). A non-Gaussian generalisation of the Airline model for robust Seasonal Adjustment Journal of Forecasting, 25(5):325--349.
Koopman, S., Jungbacker, B. and Hol, E. (2005). Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements Journal of Empirical Finance, 12(3):445--475.
Koopman, S. and Lucas, A. (2005). Business and Default Cycles for Credit Risk Journal of Applied Econometrics, 20:311--323.
Koopman, S., Lucas, A. and Klaassen, P. (2005). Empirical Credit Cycles and Capital Buffer Formation Journal of Banking and Finance, 29:3159--3179.
Koopman, S. and Luginbuhl, R. (2004). Convergence in European GDP Series Journal of Applied Econometrics, 19(5):611--636.
Koopman, S. and Lee, K. (2004). Estimating stochastic volatility models: a comparison of two importance samplers Studies in Nonlinear Dynamics and Econometrics, 8(2):1--22.
Koopman, S. and Bos, C. (2004). State space models with a common stochastic variance Journal of Business and Economic Statistics, 22(3):346--357.
Koopman, S. and Harvey, A. (2003). Computing Observation Weights for Signal Extraction and Filtering Journal of Economic Dynamics and Control, 27(7):1317--1333.
Koopman, S. and Hol Uspensky, E. (2002). The Stochastic Volatility in Mean Model: Empirical evidence from international stock markets Journal of Applied Econometrics, 17:667--689.
Koopman, S. and Franses, P. (2002). Constructing seasonally adjusted data with time-varying confidence intervals Oxford Bulletin of Economics and Statistics, 64(5):509--526.
Koopman, S. and Durbin, J. (2002). A simple and efficient smoother for state space time series analysis Biometrika, 89(3):603--616.
Harvey, A. and Koopman, S. (2000). Signal Extraction and the Formulation of Unobserved Components Models Econometrics Journal, 3:84--107.
Koopman, S. and Durbin, J. (2000). Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives Journal of the Royal Statistical Society. Series B. Statistical Methodology, 62:3--56.
Koopman, S., Shephard, N. and Doornik, J. (1999). Statistical algorithms for models in state space using SsPack 2.2 Journal of Econometrics, (2):113--166.
Sandmann, G. and Koopman, S.J. (1998). Estimation of stochastic volatility models via Monte Carlo maximum likelihood Journal of Econometrics, 87(2):271--301.
Harvey, A., Koopman, S.J. and Riani, M. (1997). The modeling and seasonal adjustment of weekly observations Journal of Business and Economic Statistics, 15(3):354--368.
Atkinson, A.C., Koopman, S.J. and Shephard, N. (1997). Detecting shocks: Outliers and breaks in time series Journal of Econometrics, 80(2):387--422.
Koopman, S.J. (1997). Exact initial kalman filtering and smoothing for nonstationary time series models Journal of the American Statistical Association, 92(440):1630--1638.
Durbin, J. and Koopman, S.J. (1997). Monte Carlo maximum likelihood estimation for non-Gaussian state space models Biometrika, 84(3):669--684.
Harvey, A. and Koopman, S.J. (1993). Forecasting hourly electricity demand using time–varying splines Journal of the American Statistical Association, 88(424):1228--1236.
Koopman, S.J. (1993). Disturbance smoother for state space models Biometrika, 80(1):117--126.
Harvey, AndrewC. and Koopman, S.J. (1992). Diagnostic checking of unobserved- components time series models Journal of Business and Economic Statistics, 10(4):377--389.
Koopman, S.J. (1992). Miscellanea: Exact score for time series models in state space form Biometrika, 79(4):823--826.