S. J. Koopman

发表

Discussion of `Particle Markov chain Monte Carlo methods – C. Andrieu, A. Doucet and R. Holenstein’ [Review of: Particle Markov chain Monte Carlo methods]

S. J. Koopman, 2010 .

The modeling and seasonal adjustment of weekly observations

Siem Jan Koopman, Marco Riani, Andrew Harvey, 1997 .

Bayesian Dynamic Modeling of High-Frequency Integer Price Changes

S. Koopman, A. Borowska, S. J. Koopman, 2018 .

Intraday Stochastic Volatility in Discrete Price Changes: The Dynamic Skellam Model

Siem Jan Koopman, Rutger Lit, André Lucas, 2017 .

The Dynamic Skellam Model with Applications

Siem Jan Koopman, Rutger Lit, Andre Lucas, 2014 .

Business and Default Cycles for Credit Risk

Siem Jan Koopman, Andre Lucas, S. Koopman, 2003 .

A simple and efficient simulation smoother for state space time series analysis

Siem Jan Koopman, James Durbin, J. Durbin, 2002 .

Dynamic Factor Models with Clustered Loadings: Forecasting Education Flows using Unemployment Data

F. Blasques, S. Koopman, S. J. Koopman, 2020, International Journal of Forecasting.

Time Series Analysis by State Space Methods: Second Edition

James Durbin, Siem Jan Koopman, J. Durbin, 2012 .

Forecasting Daily Variability of the S&P 100 Stock Index Using Historical, Realised and Implied Volatility Measurements

S. Koopman, Borus Jungbacker, Eugenie Hol Uspensky, 2004 .

Exact Initial Kalman Filtering and Smoothing for Nonstationary Time Series Models

S. Koopman, S. J. Koopman, 1997 .

Multiyear Statistical Prediction of ENSO Enhanced by the Tropical Pacific Observing System

S. Koopman, X. Rodó, D. Petrova, 2020, Journal of Climate.

Generalized Autoregressive Score Models with Applications ∗

Drew D. Creal, A. Lucas, S. J. Koopman, 2011 .

Forecasting Hourly Electricity Demand Using Time-Varying Splines

A. Harvey, S. Koopman, S. J. Koopman, 1993 .

State Space Models With a Common Stochastic Variance

Charles S. Bos, Siem Jan Koopman, S. Koopman, 2004 .

Common Business and Housing Market Cycles in the Euro Area from a Multivariate Decomposition

L. Ferrara, S. J. Koopman, 2010 .

Credit Cycles and Macro Fundamentals

Andre Lucas, Roman Kräussl, S. Koopman, 2006 .

Measuring Financial Cycles with a Model-Based Filter: Empirical Evidence for the United States and the Euro Area

Gabriele Galati, Irma Hindrayanto, S. Koopman, 2016 .

Long Memory Dynamics for Multivariate Dependence Under Heavy Tails

Faculteit der Economische Wetenschappen en Bedrijfskunde, S. Koopman, A. Lucas, 2011 .

A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations

Siem Jan Koopman, Drew D. Creal, André Lucas, 2010 .

Generalized Autoregressive Score Models

Drew D. Creal, A. Lucas, S. J. Koopman, 2012 .

The Dynamic Factor Network Model with an Application to Global Credit Risk

Falk Bräuning, S. J. Koopman, 2016 .

Forecasting the U.S. Term Structure of Interest Rates Using a Macroeconomic Smooth Dynamic Factor Model

S. Koopman, M. V. Wel, Michel van der Wel, 2012 .

Smooth Dynamic Factor Analysis with Application to the U.S. Term Structure of Interest Rates

Borus Jungbacker, Michel van der Wel, S. Koopman, 2012 .

Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models

S. Koopman, A. Lucas, M. Scharth, 2012 .

Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models

S. Koopman, Marius Ooms, G. Mesters, 2011 .

Dynamic Factor Models with Smooth Loadings for Analyzing the Term Structure of Interest Rates

Siem Jan Koopman, Michel van der Wel, Borus Jungbacker, 2009 .

Testing the assumptions behind importance sampling

Drew D. Creal, N. Shephard, S. Koopman, 2009 .

Online Appendix for Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models

A. Lucas, S. J. Koopman, Marcel Scharth, 2014 .

Periodic Seasonal Reg-ARFIMA–GARCH Models for Daily Electricity Spot Prices

Siem Jan Koopman, Marius Ooms, M. Angeles Carnero, 2007 .

Periodic Heteroskedastic Regarfima Models for Daily Electricity Spot Prices

Marius Ooms, S. Koopman, M. A. Carnero, 2003 .

Global Credit Risk: World, Country and Industry Factors

Andre Lucas, Bernd Schwaab, S. Koopman, 2016, SSRN Electronic Journal.

Credit risk around the world: Inference on world, country and industry factors

S. Koopman, B. Schwaab, S. J. Koopman, 2015 .

What drives global credit risk conditions? Inference on world, regional, and industry factors

S. Koopman, B. Schwaab, S. J. Koopman, 2014 .

Long memory with stochastic variance model: A recursive analysis for US inflation

Charles S. Bos, Siem Jan Koopman, Marius Ooms, 2014, Comput. Stat. Data Anal..

Unobserved components models in economics and finance

A. Harvey, S. Koopman, A. Harvey, 2009, IEEE Control Systems.

Long Memory Modelling of Inflation with Stochastic Variance and Structural Breaks

Charles S. Bos, S. Koopman, Marius Ooms, 2007 .

The Information in Systemic Risk Rankings

Andre Lucas, Bernd Schwaab, S. Koopman, 2015, SSRN Electronic Journal.

A Statistical Model of the Global Carbon Budget

S. Koopman, Eric Hillebrand, S. J. Koopman, 2020 .

Forecasting Football Match Results in National League Competitions Using Score-Driven Time Series Models

Siem Jan Koopman, Rutger Lit, S. Koopman, 2017, International Journal of Forecasting.

Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors

S. Koopman, Eric Hillebrand, S. J. Koopman, 2021 .

Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time Series Models

Siem Jan Koopman, Francisco Blasques, Andre Lucas, 2014 .

An Hourly Periodic State Space Model for Modelling French National Electricity Load

S. Koopman, Marius Ooms, V. Dordonnat, 2007 .

A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League

Vu, Siem Jan Koopman, Rutger Lit, 2012 .

Statistical algorithms for models in state space using SsfPack 2.2

Jurgen A. Doornik, Neil Shephard, Siem Jan Koopman, 1999 .

A Dynamic Yield Curve Model with Stochastic Volatility and Non-Gaussian Interactions: An Empirical Study of Non-Standard Monetary Policy in the Euro Area

G. Mesters, B. Schwaab, S. J. Koopman, 2014 .

Convergence in European GDP Series

S. Koopman, S. J. Koopman, R. Luginbuhl, 2003 .

Stamp 5.0 : structural time series analyser, modeller and predictor

Siem Jan Koopman, S. Koopman, S. J. Koopman, 1996 .

Time‐Varying Transition Probabilities for Markov Regime Switching Models

Francisco Blasques, Andre Lucas, F. Blasques, 2017 .

Joint Decomposition of Business and Financial Cycles: Evidence from Eight Advanced Economies*

J. D. Winter, S. J. Koopman, Irma Hindrayanto, 2021, Oxford Bulletin of Economics and Statistics.

Modeling the Business and Financial Cycle in a Multivariate Structural Time Series Model

S. J. Koopman, Irma Hindrayanto, Jasper de Winter, 2017 .

Measuring Financial Cycles in a Model-Based Analysis: Empirical Evidence for the United States and the Euro Area

S. Koopman, S. J. Koopman, Irma Hindrayanto, 2016 .

Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives

Siem Jan Koopman, James Durbin, J. Durbin, 1999 .

Regime Switches in the Volatility and Correlation of Financial Institutions

Kris Boudt, Jón Dańıelsson, S. Koopman, 2012 .

The dynamic factor network model with an application to international trade

Siem Jan Koopman, Falk Bräuning, S. Koopman, 2020, Journal of Econometrics.

Model-Based Measurement of Actual Volatility in High-Frequency Data

Borus Jungbacker, S. Koopman, Borus Jungbacker, 2005 .

Time-varying state correlations in state space models and their estimation via indirect inference

F. Palm, Stephan Smeekes, S. J. Koopman, 2021 .

Modified efficient importance sampling for partially non‐Gaussian state space models

Siem Jan Koopman, Rutger Lit, Thuy Minh Nguyen, 2019 .

Likelihood�?Based Dynamic Factor Analysis for Measurement and Forecasting

Siem Jan Koopman, Borus Jungbacker, S. Koopman, 2015 .

Time Series: State Space Methods

Siem Jan Koopman, Jacques J. F. Commandeur, S. Koopman, 2015 .

Fast Efficient Importance Sampling by State Space Methods

Rutger Lit, Siem Jan Koopman, Thuy Minh Nguyen, 2014 .

A Multivariate Periodic Unobserved Components Time Series Analysis for Sectoral U.S. Employment

S. Koopman, Marius Ooms, S. J. Koopman, 2012 .

Statistical Software for State Space Methods

Siem Jan Koopman, Marius Ooms, Jacques J. F. Commandeur, 2011 .

Monte Carlo estimation for nonlinear non-Gaussian state space models

S. Koopman, Borus Jungbacker, S. J. Koopman, 2007 .

State‐Space Methods

S. J. Koopman, 2006 .

On Importance Sampling for State Space Models

Borus Jungbacker, S. J. Koopman, 2005 .

Structural Time Series Models

Siem Jan Koopman, Andrew Harvey, A. Harvey, 2005 .

Likelihood-based Analysis for Dynamic Factor Models

S. Koopman, Borus Jungbacker, S. J. Koopman, 2008 .

A Simple and Eecient Simulation Smoother for State Space Time Series Analysis

J. Durbin, S. J. Koopman, 2001 .

SsfPack 2 . 0 : Statistical algorithms for models in state space An Ox link to underlying C code

N. Shephard, J. Doornik, S. Koopman, 1998 .

An Ox link to underlying C code

Siem Jan Koopman, Jurgen A. Doornik, Neil Shephard, 1998 .

Statistical algorithms for models in state space form: SsfPack 3.0

Siem Jan Koopman, Jurgen A. Doornik, Neil Shephard, 2008 .

Testing for Parameter Instability across Different Modeling Frameworks

Drew D. Creal, S. Koopman, A. Lucas, 2016 .

Information-theoretic optimality of observation-driven time series models for continuous responses

Siem Jan Koopman, Francisco Blasques, Andre Lucas, 2015 .

Information Theoretic Optimality of Observation Driven Time Series Models

F. Blasques, S. Koopman, A. Lucas, 2014 .

Testing for Parameter Instability in Competing Modeling Frameworks

Siem Jan Koopman, Andre Lucas, Drew D. Creal, 2014 .

Testing for Parameter Instability across Competing Modeling Frameworks ∗

Drew D. Creal, S. Koopman, A. Lucas, 2014 .

Monte Carlo maximum likelihood estimation for non-Gaussian state space models

J. Durbin, S. Koopman, S. J. Koopman, 1997 .

An Introduction to State Space Time Series Analysis

Siem Jan Koopman, Jacques J.F. Commandeur, S. Koopman, 2007 .

Time Series Analysis by State Space Methods

Siem Jan Koopman, James Durbin, J. Durbin, 2001 .

Spline Smoothing Over Difficult Regions

S. Koopman, Siem Jan Koopman, S. Wong, 2008 .

Forecasting Interest Rates with Shifting Endpoints

Jonathan H. Wright, S. Koopman, Dick J. C. van Dijk, 2012 .

Analyzing the Term Structure of Interest Rates Using the Dynamic Nelson–Siegel Model With Time-Varying Parameters

S. Koopman, Max I. P. Mallee, Michel van der Wel, 2010 .

Measuring Synchronization and Convergence of Business Cycles for the Euro Area, UK and US

S. Koopman, J. V. E. Azevedo, S. J. Koopman, 2007 .

Measuring Synchronisation and Convergence of Business Cycles

S. Koopman, S. J. Koopman, João Valle e Azevedo, 2003 .

Improving the long-lead predictability of El Niño using a novel forecasting scheme based on a dynamic components model

Desislava Petrova, Siem Jan Koopman, Joan Ballester, 2016, Climate Dynamics.

Forecasting Macroeconomic Variables Using Collapsed Dynamic Factor Analysis

Siem Jan Koopman, Falk Bräuning, S. Koopman, 2012 .

Exact score for time series models in state space form

N. Shephard, S. Koopman, S. J. Koopman, 1992 .

Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling

Siem Jan Koopman, Marius Ooms, Virginie Dordonnat, 2012, Comput. Stat. Data Anal..

Detecting shocks: Outliers and breaks in time series

Siem Jan Koopman, Neil Shephard, A. C. Atkinson, 1997 .

Outliers and Switches in Time Series

Siem Jan Koopman, A. C. Atkinson, Neil Shephard, 1994 .

Dynamic discrete copula models for high‐frequency stock price changes

Siem Jan Koopman, Rutger Lit, Anne Opschoor, 2018, Journal of Applied Econometrics.

Forecasting Economic Time Series Using Score-Driven Dynamic Models with Mixed-Data Sampling

Siem Jan Koopman, Paolo Gorgi, Mengheng Li, 2018, International Journal of Forecasting.

GENERALIZED AUTOREGRESSIVE SCORE MODELS WITH APPLICATIONS

Drew D. Creal, A. Lucas, S. J. Koopman, 2013 .

Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models

S. Koopman, A. Lucas, M. Scharth, 2012, Review of Economics and Statistics.

The stochastic volatility in mean model: empirical evidence from international stock markets

Siem Jan Koopman, S. Koopman, E. Uspensky, 2002 .

The Stochastic Volatility in Mean Model

E. Uspensky, S. J. Koopman, 2000 .

Periodic Unobserved Cycles in Seasonal Time Series with an Application to Us Unemployment

Siem Jan Koopman, Marius Ooms, Irma Hindrayanto, 2006 .

A general framework for observation-driven time-varying parameter models ∗

Drew D. Creal, A. Lucas, S. J. Koopman, 2010 .

Research Unit for Statistical and Empirical Analysis in Social Sciences (hi-stat) Global Coe Hi-stat Discussion Paper Series a General Framework for Observation Driven Time-varying Parameter Models a General Framework for Observation Driven Time-varying Parameter Models *

Andre Lucas, Drew D. Creal, S. Koopman, 2022 .

In-sample confidence bands and out-of-sample forecast bands for time-varying parameters in observation-driven models

Siem Jan Koopman, Francisco Blasques, Andre Lucas, 2016 .

Modeling Frailty-Correlated Defaults Using Many Macroeconomic Covariates

Siem Jan Koopman, Andre Lucas, Bernd Schwaab, 2009 .

Forecasting Cross-Sections of Frailty-Correlated Default

S. Koopman, A. Lucas, B. Schwaab, 2008 .

The Multi-State Latent Factor Intensity Model for Credit Rating Transitions

Siem Jan Koopman, Andre Lucas, A. Lucas, 2005 .

Diagnostic checking and intra-daily effects in time series models

Siem Jan Koopman, S. Koopman, S. J. Koopman, 1992 .

Intervention time series analysis of crime rates: The case of sentence reform in Virginia

S. Koopman, J. Commandeur, S. Vujić, 2016, Economic Modelling.

Intervention Time Series Analysis of Crime Rates

S. Koopman, Sanjeev Sridharan, S. Vujić, 2003 .

Time series analysis of non‐Gaussian observations based on state space models from both classical and Bayesian perspectives

J. Durbin, S. J. Koopman, 1998 .

Business and Default Cycles for Credit Risk

A. Lucas, S. J. Koopman, 2003 .

A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk

Siem Jan Koopman, André Lucas, S. Koopman, 2003 .

Beta Observation-Driven Models With Exogenous Regressors: A Joint Analysis of Realized Correlation and Leverage Effects

S. Koopman, P. Gorgi, S. J. Koopman, 2020, SSRN Electronic Journal.

Estimation of stochastic volatility models via Monte Carlo maximum likelihood

Siem Jan Koopman, S. Koopman, G. Sandmann, 1998 .

Sensitivity of large dengue epidemics in Ecuador to long-lead predictions of El Niño

Xavier Rodó, Siem Jan Koopman, Rachel Lowe, 2019, Climate Services.

Bayesian Risk Forecasting for Long Horizons

Lennart F. Hoogerheide, S. Koopman, A. Borowska, 2019, SSRN Electronic Journal.

A Forty Year Assessment of Forecasting the Boat Race

Siem Jan Koopman, Geert Mesters, G. Mesters, 2012 .

Forecasting the Boat Race

Siem Jan Koopman, Geert Mesters, S. Koopman, 2015 .

Realized Wishart-Garch: A Score-Driven Multi-Asset Volatility Model

S. Koopman, P. Janus, P. Hansen, 2016 .

Stationarity and Ergodicity of Univariate Generalized Autoregressive Score Processes

F. Blasques, S. Koopman, A. Lucas, 2012 .

Structural time series models in medicine

A Harvey, S J Koopman, A. Harvey, 1996, Statistical methods in medical research.

Finding the European crime drop using a panel data model with stochastic trends

S. J. Koopman, Ilka van de Werve, 2022 .

Model-Based Business Cycle and Financial Cycle Decomposition for Europe and the U.S.

A. Lucas, R. Lit, S. J. Koopman, 2016 .

Optimal Formulations for Nonlinear Autoregressive Processes

F. Blasques, S. Koopman, A. Lucas, 2014 .

Forecasting economic time series using unobserved components time series models

Siem Jan Koopman, Marius Ooms, S. Koopman, 2011 .

Extracting a Robust U.S. Business Cycle Using a Time-Varying Multivariate Model-Based Bandpass Filter

Drew D. Creal, S. J. Koopman, E. Zivot, 2010 .

The Effect of the Great Moderation on the U.S. Business Cycle in a Time-Varying Multivariate Trend-Cycle Model

Drew D. Creal, S. Koopman, S. J. Koopman, 2008 .

Short-term forecasting of business cycle turning points : a mixed-frequency Markov-switching dynamic factor analysis

S. Koopman, Matías Pacce, S. J. Koopman, 2016 .

Analysis of historical time series with messy features: the case of commodity prices in Babylonia

Lennart F. Hoogerheide, S. Koopman, S. J. Koopman, 2014 .

Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State-Space Models

S. Koopman, A. Lucas, M. Scharth, 2012 .

A non-Gaussian generalization of the Airline model for robust seasonal adjustment

Siem Jan Koopman, John A. D. Aston, J. Aston, 2006 .

Accelerating score-driven time series models

F. Blasques, P. Gorgi, S. J. Koopman, 2019, Journal of Econometrics.

Accelerating GARCH and Score-Driven Models: Optimality, Estimation and Forecasting

F. Blasques, S. Koopman, P. Gorgi, 2017 .

Empirical Bayes Methods for Dynamic Factor Models

S. Koopman, G. Mesters, S. J. Koopman, 2014, Review of Economics and Statistics.

Generalized Dynamic Panel Data Models with Random Effects for Cross-Section and Time

S. Koopman, G. Mesters, S. J. Koopman, 2012 .

Exact maximum likelihood estimation for non-stationary periodic time series models

Siem Jan Koopman, Marius Ooms, Irma Hindrayanto, 2010, Comput. Stat. Data Anal..

Forecasting daily time series using periodic unobserved components time series models

Siem Jan Koopman, Marius Ooms, Marius Ooms, 2006, Comput. Stat. Data Anal..

Computing Observation Weights for Signal Extraction and Filtering

Siem Jan Koopman, Andrew Harvey, A. Harvey, 2003 .

Time Series Modelling of Daily Tax Revenues

S. Koopman, Marius Ooms, S. J. Koopman, 2001 .

Disturbance smoother for state space models

S. Koopman, S. J. Koopman, 1993 .

MESSY TIME SERIES: A UNIFIED APPROACH

Siem Jan Koopman, Andrew Harvey, Jeremy Penzer, 2009 .

Trend-Cycle Decomposition Models with Smooth-Transition Parameters: Evidence from U.S. Economic Time Series

S. Koopman, Kai Ming Lee, S. J. Koopman, 2006 .

Fast Filtering and Smoothing for Multivariate State Space Models

Siem Jan Koopman, James Durbin, J. Durbin, 2000 .

MESSY TIME SERIES

Siem Jan Koopman, Andrew Harvey, Jeremy Penzer, 1999 .

Maximum Likelihood Estimation for Dynamic Factor Models with Missing Data

Borus Jungbacker, Siem Jan Koopman, Michel van der Wel, 2011 .

Maximum likelihood estimation of parameters

Siem Jan Koopman, James Durbin, J. Durbin, 2012 .

Joint Bayesian Analysis of Parameters and States in Nonlinear non-Gaussian State Space Models

Lennart F. Hoogerheide, Siem Jan Koopman, Andre Lucas, 2017 .

Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models

Lennart F. Hoogerheide, S. Koopman, A. Lucas, 2016 .

Maximum Likelihood Estimation for Score-Driven Models

F. Blasques, S. Koopman, A. Lucas, 2014, Journal of Econometrics.

Maximum Likelihood Estimation for Generalized Autoregressive Score Models

F. Blasques, S. Koopman, A. Lucas, 2014 .

Economic Trends and Cycles in Crime: A Study for England and Wales

S. Koopman, J. Commandeur, S. Vujić, 2012 .

Structural Time Series Analyser, Modeller and Predictor: STAMP 8.2.

Siem Jan Koopman, Jurgen A. Doornik, Andrew Harvey, 2009 .

Estimating systematic continuous‐time trends in recidivism using a non‐Gaussian panel data model

S. Koopman, A. Lucas, K. Montfort, 2007 .

Modelling trigonometric seasonal components for monthly economic time series

J. Aston, Marius Ooms, S. J. Koopman, 2010 .

Spot Variance Path Estimation and Its Application to High Frequency Jump Testing

Charles S. Bos, Siem Jan Koopman, Pawel Janus, 2009 .

Dynamic factors in state-space models for hourly electricity load signal decomposition and forecasting

Siem Jan Koopman, Marius Ooms, Virginie Dordonnat, 2009, 2009 IEEE Power & Energy Society General Meeting.

State space modelling in macroeconomics and finance using SsfPack in S+Finmetrics

Siem Jan Koopman, Eric Zivot, Eric Zivot, 2004 .

Models with Time-Varying Mean and Variance: A Robust Analysis of U.S. Industrial Production

Charles S. Bos, S. Koopman, S. J. Koopman, 2010 .

Structural Intervention Time Series Analysis of Crime Rates: The Impact of Sentence Reform in Virginia

S. Koopman, J. Commandeur, S. Vujić, 2012 .

Likelihood functions for state space models with diffuse initial conditions

Marc K. Francke, S. Koopman, A. de Vos, 2006 .

Forecasting in a Changing World: from the Great Recession to the COVID-19 Pandemic

F. Blasques, S. J. Koopman, Zhaokun Zhang, 2021 .

Measuring Asymmetric Stochastic Cycle Components in U.S. Macroeconomic Time Series

Kai Ming Lee, S. J. Koopman, 2005 .

Measuring Asymmetric Stochastic Cycle Components

S. Koopman, Kai Ming Lee, S. J. Koopman, 2005 .

Unobserved Components Models in Economics and Finance THE ROLE OF THE KALMAN FILTER IN TIME SERIES ECONOMETRICS

A. Harvey, S. Koopman, S. J. Koopman, 2010 .

Feasible Invertibility Conditions and Maximum Likelihood Estimation for Observation-Driven Models

F. Blasques, S. Koopman, O. Wintenberger, 2016 .

Modeling Dynamic Volatilities and Correlations under Skewness and Fat Tails

Drew D. Creal, A. Lucas, Xin Zhang, 2011 .

Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk

Siem Jan Koopman, Drew D. Creal, André Lucas, 2011, Review of Economics and Statistics.

A New Model for Dynamic Correlations under Skewness and Fat Tails

Drew D. Creal, A. Lucas, Xin Zhang, 2011 .

Generalized autoregressive score models with applications ∗

Drew D. Creal, S. Koopman, A. Lucas, 2010 .

Special Issue on Nonlinear Modelling and Financial Econometrics

Siem Jan Koopman, Christian Francq, Alessandra Amendola, 2006, Comput. Stat. Data Anal..

Using rapid damage observations for Bayesian updating of hurricane vulnerability functions: A case study of Hurricane Dorian using social media

Jens A. de Bruijn, S. Koopman, J. Aerts, 2022, International Journal of Disaster Risk Reduction.

Dynamic Factor Models With Macro, Frailty, and Industry Effects for U.S. Default Counts: The Credit Crisis of 2008

Siem Jan Koopman, Andre Lucas, Bernd Schwaab, 2012 .

Macro, industry and frailty effects in defaults: The 2008 credit crisis in perspective

A. Lucas, B. Schwaab, S. J. Koopman, 2010 .

The 2008 Credit Crisis in Perspective Macro, Industry and Frailty Effects in Defaults: the 2008 Credit Crisis in Perspective * Tinbergen Institute Duisenberg School of Finance Macro, Industry and Frailty Factors in Defaults: the 2008 Credit Crisis in Perspective

B. Schwaab, S. J. Koopman, Jan Siem, 2010 .

Macro, Industry and Frailty Effects in Defaults: the 2008 Credit Crisis in Perspective * Macro, Industry and Frailty Factors of Defaults: the 2008 Credit Crisis in Perspective

B. Schwaab, S. J. Koopman, Jan Siem, 2009 .

Systematic risk diagnostics

S. Koopman, A. Lucas, B. Schwaab, 2010 .

Mixed Measurement Dynamic Factor Models

Siem Jan Koopman, Andre Lucas, Drew D. Creal, 2010 .

Testing the Assumptions Behind the use of Importance Sampling

Siem Jan Koopman, Neil Shephard, N. Shephard, 2002 .

Modelling bid-ask spreads in competitive dealership markets

Siem Jan Koopman, Hung-Neng Lai, Hung-Neng Lai, 1998 .

Discussion of ‘MCMC‐based inference’ by R. Paap

Siem Jan Koopman, S. Koopman, S. J. Koopman, 2002 .

Trend analysis of the airborne fraction and sink rate of anthropogenically released CO2

Eric Hillebrand, S. J. Koopman, Mikkel Bennedsen, 2018, Biogeosciences.

Modeling Around-the-Clock Price Discovery for Cross-Listed Stocks Using State Space Methods

Siem Jan Koopman, André Lucas, S. Koopman, 2004 .

Round-the-Clock Price Discovery for Cross-Listed Stocks

S. Koopman, A. Lucas, A. Menkveld, 2003 .

TI 15-070 / III / DSF 94 The Information in Systemic Risk Rankings

S. Koopman, A. Lucas, B. Schwaab, 2015 .

*Periodic Unobserved Cycles in Seasonal Time Series with an Application to Us Unemployment

Marius Ooms, S. J. Koopman, Irma Hindrayanto, 2009 .

Signal extraction and the formulation of unobserved components models

Siem Jan Koopman, Andrew Harvey, A. Harvey, 2000 .

Time Series Analysis by State Space Methods: Second Edition

J. Durbin, S. J. Koopman, 2012 .

Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data

Zhaoyong Zhang, Siem Jan Koopman, Francisco Blasques, 2016 .

Modeling Round-the-Clock Price Discovery for Cross-Listed Stocks Using State Space Methods

S. Koopman, A. Lucas, A. Menkveld, 2007 .

Nowcasting and Forecasting Economic Growth in the Euro Area Using Principal Components

S. Koopman, S. J. Koopman, Irma Hindrayanto, 2014 .

Joint Modelling and Estimation of Global and Local Cross-Sectional Dependence in Large Panels

S. J. Koopman, Julia Schaumburg, Quint Wiersma, 2021 .

Systemic Risk Diagnostics: Coincident Indicators and Early Warning Signals

S. Koopman, A. Lucas, B. Schwaab, 2010, SSRN Electronic Journal.

Temporal, Spatial, Economic and Crime Factors in Illicit Drug Usage Across European Cities

S. Koopman, B. Kasprzyk-Hordern, J. Commandeur, 2014 .