Siem Jan Koopman

Names

first:	Siem Jan
last:	Koopman

Contact

email:
homepage:	http://sjkoopman.net
phone:	+31 20 598 6019
postal address:	Department of Econometrics, School of Business and Economics, Vrije Universiteit Amsterdam, De Boelelaan 1105, NL-1081 HV Amsterdam, The Netherlands

Affiliations

• Vrije Universiteit Amsterdam → School of Business and Economics → Afdeling Econometrie and Operations Research (weight: 90%)
  • website
  • location: Amsterdam, Netherlands
• Tinbergen Instituut (weight: 10%)
  • website
  • location: Amsterdam, Netherlands

Research profile

author of:

• Computing Observation Weights for Signal Extraction and Filtering
  by A. C. Harvey & Siem Jan Koopman
• Maximum Likelihood Estimation of Stochastic Volatility Models
  by G. Sandmann & Siem Jan Koopman
• Testing the Assumptions Behind the Use of Importance Sampling
  by Siem Jan Koopman & Neil Shephard
• An efficient and simple simulation smoother for state space time series analysis
  by J. Durbin and S. J. Koopman
• Fast Estimation of Parameters in State Space Models
  by Siem Jan Koopman
• Diagnostic Checking of Unobserved-Components Time Series Models.
  by Harvey, Andrew C. & Koopman, Siem Jan
• The Modeling and Seasonal Adjustment of Weekly Observations.
  by Harvey, Andrew & Koopman, Siem Jan & Riani, Marco
• Statistical algorithms for models in state space using SsfPack 2.2
  by SIEM JAN KOOPMAN & NEIL SHEPHARD & JURGEN A. DOORNIK
• Signal extraction and the formulation of unobserved components models
  by ANDREW HARVEY & SIEM JAN KOOPMAN
• Filtering and smoothing of state vector for diffuse state‐space models
  by S. J. Koopman & J. Durbin
• Estimation of stochastic volatility models via Monte Carlo maximum likelihood
  by Sandmann, Gleb & Koopman, Siem Jan
• Forecasting daily variability of the S\&P 100 stock index using historical, realised and implied volatility measurements
  by Eugenie Hol & Siem Jan Koopman & Borus Jungbacker
• Computing observation weights for signal extraction and filtering
  by Koopman, Siem Jan & Harvey, Andrew
• Convergence in European GDP series: a multivariate common converging trend-cycle decomposition
  by Rob Luginbuhl & Siem Jan Koopman
• Detecting shocks: Outliers and breaks in time series
  by Atkinson, A. C. & Koopman, S. J. & Shephard, N.
• Periodic Heteroskedastic RegARFIMA models for daily electricity spot prices
  by Marius Ooms & M. Angeles Carnero & Siem Jan Koopman
• Time-Series Modelling of Daily Tax Revenues
  by Marius Ooms & Björn de Groot & Siem Jan Koopman
• The stochastic volatility in mean model: empirical evidence from international stock markets
  by Siem Jan Koopman & Eugenie Hol Uspensky
• Business and default cycles for credit risk
  by André Lucas & Siem Jan Koopman
• Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements
  by Koopman, Siem Jan & Jungbacker, Borus & Hol, Eugenie
• Empirical credit cycles and capital buffer formation
  by Koopman, Siem Jan & Lucas, Andre & Klaassen, Pieter
• A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk
  by Siem Jan Koopman & André Lucas & Robert J. Daniels
• A non-Gaussian generalization of the Airline model for robust seasonal adjustment
  by SIEM JAN KOOPMAN & JOHN A. D. ASTON
• Periodic Seasonal Reg-ARFIMAGARCH Models for Daily Electricity Spot Prices
  by Koopman, Siem Jan & Ooms, Marius & Carnero, M. Angeles
• Special Issue on Nonlinear Modelling and Financial Econometrics
  by Amendola, Alessandra & Francq, Christian & Koopman, Siem Jan
• State Space Models With a Common Stochastic Variance
  by Koopman S. J. & Bos C. S.
• Tracking the Business Cycle of the Euro Area: A Multivariate Model-Based Bandpass Filter
  by Valle e Azevedo, Joao & Koopman, Siem Jan & Rua, Antonio
• Time series analysis of non‐Gaussian observations based on state space models from both classical and Bayesian perspectives
  by J. Durbin & S. J. Koopman
• Discussion of ‘MCMC‐based inference’ by R. Paap
  by S. J. Koopman
• Forecasting daily time series using periodic unobserved components time series models
  by Koopman, Siem Jan & Ooms, Marius
• Time Series Modelling of Daily Tax Revenues
  by Siem Jan Koopman & Marius Ooms
• Interaction between structural and cyclical shocks in production and employment
  by F. Butter & S. Koopman
• The multi-state latent factor intensity model for credit rating transitions
  by Koopman, Siem Jan & Lucas, Andre & Monteiro, Andre
• Model‐based measurement of latent risk in time series with applications
  by Frits Bijleveld & Jacques Commandeur & Phillip Gould & Siem Jan Koopman
• Estimating systematic continuous‐time trends in recidivism using a non‐Gaussian panel data model
  by Siem Jan Koopman & Marius Ooms & André Lucas & Kees van Montfort & Victor Van Der Geest
• Measuring Synchronization and Convergence of Business Cycles for the Euro area, UK and US*
  by Siem Jan Koopman & João Valle E. Azevedo
• Long memory modelling of inflation with stochastic variance and structural breaks
  by Charles S. Bos & Siem Jan Koopman & Marius Ooms
• Modeling Around-the-Clock Price Discovery for Cross-Listed Stocks Using State Space Methods
  by Menkveld, Albert J. & Koopman, Siem Jan & Lucas, Andre
• Monte Carlo Estimation for Nonlinear Non-Gaussian State Space Models
  by Borus Jungbacker & Siem Jan Koopman
• Credit cycles and macro fundamentals
  by Koopman, Siem Jan & Kräussl, Roman & Lucas, André & Monteiro, André B.
• An hourly periodic state space model for modelling French national electricity load
  by Dordonnat, V. & Koopman, S. J. & Ooms, M. & Dessertaine, A. & Collet, J.
• A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk
  by Koopman, Siem Jan & Lucas, AndrÃ
• A General Framework for Observation Driven Time-Varying Parameter Models
  by Drew Creal & Siem Jan Koopman & Andre Lucas
• Testing the assumptions behind importance sampling
  by Koopman, Siem Jan & Shephard, Neil & Creal, Drew
• Seasonality with trend and cycle interactions in unobserved components models
  by Siem Jan Koopman & Kai Ming Lee
• Estimating Stochastic Volatility Models: A Comparison of Two Importance Samplers
  by Lee Kai Ming & Koopman Siem Jan
• Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment*
  by Siem Jan Koopman & Marius Ooms & Irma Hindrayanto
• Smooth Dynamic Factor Analysis with an Application to the U.S. Term Structure of Interest Rates
  by Borus Jungbacker & Siem Jan Koopman & Michel van der Wel
• Extracting a robust US business cycle using a time-varying multivariate model-based bandpass filter
  by Drew Creal & Siem Jan Koopman & Eric Zivot
• Common business and housing market cycles in the Euro area from a multivariate decomposition.
  by Ferrara, L. & Koopman, S. J.
• Tracking Growth and the Business Cycle: a Stochastic Common Cycle Model for the Euro Area
  by António Rua & João Valle e Azevedo & Siem Jan Koopman
• Exact maximum likelihood estimation for non-stationary periodic time series models
  by Hindrayanto, Irma & Koopman, Siem Jan & Ooms, Marius
• Multivariate non‐linear time series modelling of exposure and risk in road safety research
  by Frits Bijleveld & Jacques Commandeur & Siem Jan Koopman & Kees van Montfort
• Extracting a Robust U.S. Business Cycle Using a Time-Varying Multivariate Model-Based Bandpass Filter
  by Drew Creal & Siem Jan Koopman & Eric Zivot
• Likelihood functions for state space models with diffuse initial conditions
  by Marc K. Francke & Siem Jan Koopman & Aart F. De Vos
• Analyzing the Term Structure of Interest Rates Using the Dynamic Nelson–Siegel Model With Time-Varying Parameters
  by Koopman, Siem Jan & Mallee, Max I. P. & Van der Wel, Michel
• Kalman filtering and smoothing for model‐based signal extraction that depend on time‐varying spectra
  by Siem Jan Koopman & Soon Yip Wong
• Monte Carlo Likelihood Estimation for Three Multivariate Stochastic Volatility Models
  by Borus Jungbacker & Siem Jan Koopman
• Maximum likelihood estimation for dynamic factor models with missing data
  by Jungbacker, B. & Koopman, S. J. & van der Wel, M.
• An Introduction to State Space Time Series Analysis
  by Commandeur, Jacques J. F. & Koopman, Siem Jan
• Systemic risk diagnostics: coincident indicators and early warning signals
  by Schwaab, Bernd & Koopman, Siem Jan & Lucas, André
• Modeling frailty-correlated defaults using many macroeconomic covariates
  by Koopman, Siem Jan & Lucas, André & Schwaab, Bernd
• Time Series Analysis by State Space Methods
  by Durbin, James & Koopman, Siem Jan
• Exponentionally weighted methods for forecasting intraday time series with multiple seasonal cycles: Comments
  by Koopman, S. J. & Ooms, M.
• Spot Variance Path Estimation and Its Application to High-Frequency Jump Testing
  by Charles S. Bos & Pawe & lstrok; Janus & Siem Jan Koopman
• A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations
  by Creal, Drew & Koopman, Siem Jan & Lucas, AndrÃ
• Time Series Analysis by State Space Methods
  by Durbin, James & Koopman, Siem Jan
• Dynamic factor models with macro, frailty and industry effects for US default counts: the credit crisis of 2008
  by Schwaab, Bernd & Koopman, Siem Jan & Lucas, André
• Modelling trigonometric seasonal components for monthly economic time series
  by Irma Hindrayanto & John A. D. Aston & Siem Jan Koopman & Marius Ooms
• The Analysis of Stochastic Volatility in the Presence of Daily Realized Measures
  by Siem Jan Koopman & Marcel Scharth
• Economic Trends and Cycles in Crime: A Study for England and Wales
  by Vujić Sunčica & Koopman Siem Jan & Commandeur J. F.
• Dynamic Factor Models With Macro, Frailty, and Industry Effects for U.S. Default Counts: The Credit Crisis of 2008
  by Siem Jan Koopman & André Lucas & Bernd Schwaab
• Credit cycles and macro fundamentals
  by Koopman, Siem Jan & Kräussl, Roman & Lucas, André
• Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling
  by Dordonnat, Virginie & Koopman, Siem Jan & Ooms, Marius
• GENERALIZED AUTOREGRESSIVE SCORE MODELS WITH APPLICATIONS
  by Drew Creal & Siem Jan Koopman & André Lucas
• Forecasting the US term structure of interest rates using a macroeconomic smooth dynamic factor model
  by Koopman, Siem Jan & van der Wel, Michel
• Nowcasting and forecasting economic growth in the euro area using principal components
  by Irma Hindrayanto & Siem Jan Koopman & Jasper de Winter
• Constructing seasonally adjusted data with time-varying confidence intervals
  by Koopman, S. J. & Franses, Ph. H. B. F.
• Forecasting interest rates with shifting endpoints
  by Dick Dijk & Siem Jan Koopman & Michel Wel & Jonathan H. Wright
• Long memory with stochastic variance model: A recursive analysis for US inflation
  by Bos, Charles S. & Koopman, Siem Jan & Ooms, Marius
• Nowcasting and forecasting global financial sector stress and credit market dislocation
  by Schwaab, Bernd & Koopman, Siem Jan & Lucas, André
• Observation driven mixed-measurement dynamic factor models with an application to credit risk
  by Schwaab, Bernd & Koopman, Siem Jan & Lucas, André & Creal, Drew
• Generalized dynamic panel data models with random effects for cross-section and time
  by Mesters, G. & Koopman, S. J.
• Forecasting macroeconomic variables using collapsed dynamic factor analysis
  by Bräuning, Falk & Koopman, Siem Jan
• Interaction between supply and demand in production and employment
  by Butter, Frank A. G. den & Koopman, S. J.
• A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations
  by Drew Creal & Siem Jan Koopman & André Lucas
• Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models
  by Geert Mesters & Siem Jan Koopman & Marius Ooms
• Model-based Business Cycle and Financial Cycle Decomposition for Europe and the U.S.
  by Siem Jan Koopman & Rutger Lit & Andre Lucas
• Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models
  by G. Mesters & S. J. Koopman & M. Ooms
• Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State-Space Models
  by Siem Jan Koopman & André Lucas & Marcel Scharth
• Macro, Industry and Frailty Effects in Defaults: The 2008 Credit Crisis in Perspective
  by Siem Jan Koopman & Andre Lucas & Bernd Schwaab
• Multivariate Structural Time Series Models - (Now published in 'System Dynamics in Economic and Financial Models', CHeij, H Schumacher, B Hanzon and C Praagman (eds.) John Wiley & Sons, Chichester (1997), pp.269-298.)
  by Andrew C. Harvey & Siem Jan Koopman
• Nowcasting and Forecasting Economic Growth in the Euro Area using Principal Components
  by Irma Hindrayanto & Siem Jan Koopman & Jasper de Winter
• The Multi-State Latent Factor Intensity Model for Credit Rating Transitions
  by Siem Jan Koopman & André Lucas & André Monteiro
• Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk
  by Drew Creal & Bernd Schwaab & Siem Jan Koopman & Andre Lucas
• On Importance Sampling for State Space Models
  by Borus Jungbacker & Siem Jan Koopman
• Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk
  by Drew Creal & Bernd Schwaab & Siem Jan Koopman & Andr� Lucas
• Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models
  by Siem Jan Koopman & Andre Lucas & Marcel Scharth
• Likelihood-based Analysis for Dynamic Factor Models
  by Borus Jungbacker & Siem Jan Koopman
• Models with Time-varying Mean and Variance: A Robust Analysis of U.S. Industrial Production
  by Charles S. Bos & Siem Jan Koopman
• Modeling Trigonometric Seasonal Components for Monthly Economic Time Series
  by Irma Hindrayanto & John A. D. Aston & Siem Jan Koopman & Marius Ooms
• Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices
  by Siem Jan Koopman & Marius Ooms & M. Angeles Carnero
• Model-based Measurement of Actual Volatility in High-Frequency Data
  by B. Jungbacker & S. J. Koopman
• Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment
  by Siem Jan Koopman & Marius Ooms & Irma Hindrayanto
• Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models
  by Siem Jan Koopman & André Lucas & Marcel Scharth
• Modeling Dynamic Volatilities and Correlations under Skewness and Fat Tails
  by Xin Zhang & Drew Creal & Siem Jan Koopman & Andre Lucas
• Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices
  by M. Angeles Carnero & Siem Jan Koopman & Marius Ooms
• Optimal Formulations for Nonlinear Autoregressive Processes
  by Francisco Blasques & Siem Jan Koopman & André Lucas
• Model-based Measurement of Latent Risk in Time Series with Applications
  by Frits Bijleveld & Jacques Commandeur & Phillip Gould & Siem Jan Koopman
• The Modelling and Seasonal Adjustment of Weekly Observations - (Now published in 'Journal of Business and Economic Statistics', 15 (1997), pp.354-368.)
  by Andrew C. Harvey & Siem Jan Koopman & Marco Riani
• Pro-Cyclicality, Empirical Credit Cycles, and Capital Buffer Formation
  by Siem Jan Koopman & André Lucas & Pieter Klaassen
• Measuring Financial Cycles in a Model-Based Analysis: Empirical Evidence for the United States and the Euro Area
  by Gabriele Galati & Irma Hindrayanto & Siem Jan Koopman & Marente Vlekke
• Measuring financial cycles in a model-based analysis: Empirical evidence for the United States and the euro area
  by Galati, Gabriele & Hindrayanto, Irma & Koopman, Siem Jan & Vlekke, Marente
• Measuring Asymmetric Stochastic Cycle Components in U.S. Macroeconomic Time Series
  by Siem Jan Koopman & Kai Ming Lee
• Measuring Synchronisation and Convergence of Business Cycles
  by Siem Jan Koopman & Joao Valle e Azevedo
• Messy Time Series: A Unified Approach - (Now published in 'Advances in Econometrics', 13 (1998)pp.103-143.)
  by Andrew C. Harvey & Siem Jan Koopman & J. Penzer
• Intraday Stochastic Volatility in Discrete Price Changes: the Dynamic Skellam Model
  by Siem Jan Koopman & Rutger Lit & Andre Lucas
• Measuring financial cycles with a model-based filter: Empirical evidence for the United States and the euro area
  by Gabriele Galati & Irma Hindrayanto & Siem Jan Koopman & Marente Vlekke
• Maximum Likelihood Estimation for Score-Driven Models
  by Francisco Blasques & Siem Jan Koopman & Andre Lucas
• Maximum likelihood estimation for dynamic factor models with missing data
  by B. Jungbacker & S. J. Koopman & M. van der Wel
• Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models
  by Siem Jan Koopman & Andre Lucas & Marcel Scharth
• Intraday Stock Price Dependence using Dynamic Discrete Copula Distributions
  by Siem Jan Koopman & Rutger Lit & André Lucas
• Seasonality with Trend and Cycle Interactions in Unobserved Components Models
  by Siem Jan Koopman & Kai Ming Lee
• Round-the-Clock Price Discovery for Cross-Listed Stocks: US-Dutch Evidence
  by Albert J. Menkveld & Siem Jan Koopman & André Lucas
• Long memory dynamics for multivariate dependence under heavy tails
  by Janus, Paweł & Koopman, Siem Jan & Lucas, André
• Likelihood‐based dynamic factor analysis for measurement and forecasting
  by Borus Jungbacker & Siem Jan Koopman
• In-Sample Confidence Bands and Out-of-Sample Forecast Bands for Time-Varying Parameters in Observation Driven Models
  by Francisco Blasques & Siem Jan Koopman & Katarzyna Lasak & André Lucas
• Long Memory Modelling of Inflation with Stochastic Variance and Structural Breaks
  by C. S. Bos & S. J. Koopman & M. Ooms
• Realized Wishart-GARCH: A Score-driven Multi-Asset Volatility Model
  by Peter Reinhard Hansen & Pawel Janus & Siem Jan Koopman
• Maximum Likelihood Estimation for correctly Specified Generalized Autoregressive Score Models: Feedback Effects, Contraction Conditions and Asymptotic Properties
  by Francisco Blasques & Siem Jan Koopman & André Lucas
• In-Sample Bounds for Time-Varying Parameters of Observation Driven Models
  by Francisco Blasques & Siem Jan Koopman & Katarzyna Lasak & André Lucas
• Low Frequency and Weighted Likelihood Solutions for Mixed Frequency Dynamic Factor Models
  by Francisco Blasques & Siem Jan Koopman & Max Mallee
• In-sample confidence bands and out-of-sample forecast bands for time-varying parameters in observation-driven models
  by Blasques, Francisco & Koopman, Siem Jan & Łasak, Katarzyna & Lucas, André
• Long Memory Dynamics for Multivariate Dependence under Heavy Tails
  by Pawel Janus & Siem Jan Koopman & André Lucas
• Information-theoretic optimality of observation-driven time series models for continuous responses
  by F. Blasques & S. J. Koopman & A. Lucas
• Information Theoretic Optimality of Observation Driven Time Series Models
  by Francisco Blasques & Siem Jan Koopman & André Lucas
• Likelihood Functions for State Space Models with Diffuse Initial Conditions
  by Marc K. Francke & Siem Jan Koopman & Aart de Vos
• SMOOTH DYNAMIC FACTOR ANALYSIS WITH APPLICATION TO THE US TERM STRUCTURE OF INTEREST RATES
  by Borus Jungbacker & Siem Jan Koopman & Michel Wel
• Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models
  by István Barra & Lennart Hoogerheide & Siem Jan Koopman & André Lucas
• Intervention time series analysis of crime rates: The case of sentence reform in Virginia
  by Vujić, Sunčica & Commandeur, Jacques J. F. & Koopman, Siem Jan
• Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data
  by Blasques, F. & Koopman, S. J. & Mallee, M. & Zhang, Z.
• Interaction between Supply and Demand Shocks in Production and Employment
  by F. A. G. den Butter & S. J. Koopman
• The information in systemic risk rankings
  by Schwaab, Bernd & Koopman, Siem Jan & Lucas, André & Nucera, Federico
• Spillover dynamics for systemic risk measurement using spatial financial time series models
  by Blasques, Francisco & Koopman, Siem Jan & Lucas, Andre & Schaumburg, Julia
• The Information in Systemic Risk Rankings
  by Federico Nucera & Bernd Schwaab & Siem Jan Koopman & André Lucas
• The information in systemic risk rankings
  by Nucera, Federico & Schwaab, Bernd & Koopman, Siem Jan & Lucas, André
• Spillover dynamics for systemic risk measurement using spatial financial time series models
  by Blasques, Francisco & Koopman, Siem Jan & Lucas, Andre & Schaumburg, Julia
• Global credit risk: world country and industry factors
  by Schwaab, Bernd & Koopman, Siem Jan & Lucas, André
• Global Credit Risk: World, Country and Industry Factors
  by Bernd Schwaab & Siem Jan Koopman & André Lucas
• Generalized Autoregressive Method of Moments
  by Drew Creal & Siem Jan Koopman & André Lucas & Marcin Zamojski
• Spillover Dynamics for Systemic Risk Measurement using Spatial Financial Time Series Models
  by Francisco Blasques & Siem Jan Koopman & Andre Lucas & Julia Schaumburg
• Spline Smoothing over Difficult Regions
  by Siem Jan Koopman & Soon Yip Wong
• Tracking Growth and the Business Cycle: a Stochastic Common Cycle Model for the Euro Area
  by Joao Valle e Azevedo & Siem Jan Koopman & Antonio Rua
• Intervention Time Series Analysis of Crime Rates
  by Sanjeev Sridharan & Suncica Vujic & Siem Jan Koopman
• Forecasting and nowcasting economic growth in the euro area using factor models
  by Hindrayanto, Irma & Koopman, Siem Jan & de Winter, Jasper
• Spot Variance Path Estimation and its Application to High Frequency Jump Testing
  by Charles S. Bos & Pawel Janus & Siem Jan Koopman
• Generalized Dynamic Panel Data Models with Random Effects for Cross-Section and Time
  by Geert Mesters & Siem Jan Koopman
• Temporal, Spatial, Economic and Crime Factors in Illicit Drug Usage across European Cities
  by Jacques J. F. Commandeur & Suncica Vujic & Siem Jan Koopman & Barbara Kasprzyk-Hordern
• Forecasting Macroeconomic Variables using Collapsed Dynamic Factor Analysis
  by Falk Brauning & Siem Jan Koopman
• Forecasting the U.S. Term Structure of Interest Rates using a Macroeconomic Smooth Dynamic Factor Model
  by Siem Jan Koopman & Michel van der Wel
• Forecasting Interest Rates with Shifting Endpoints
  by Dick van Dijk & Siem Jan Koopman & Michel van der Wel & Jonathan H. Wright
• Forecasting Cross-Sections of Frailty-Correlated Default
  by Siem Jan Koopman & André Lucas & Bernd Schwaab
• Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility
  by Eugenie Hol & Siem Jan Koopman
• Time Varying Transition Probabilities for Markov Regime Switching Models
  by Marco Bazzi & Francisco Blasques & Siem Jan Koopman & Andre Lucas
• Forecasting Daily Variability of the S&P 100 Stock Index using Historical, Realised and Implied Volatility Measurements
  by Siem Jan Koopman & Borus Jungbacker & Eugenie Hol
• Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series
  by Siem Jan Koopman & Charles S. Bos
• Time Series Modelling of Daily Tax Revenues
  by Siem Jan Koopman & Marius Ooms
• Testing for Parameter Instability in Competing Modeling Frameworks
  by Francesco Calvori & Drew Creal & Siem Jan Koopman & Andre Lucas
• Systemic Risk Diagnostics
  by Bernd Schwaab & Andre Lucas & Siem Jan Koopman
• Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models *
  by F. Blasques & P. Gorgi & S. Koopman & O. Wintenberger
• Structural Intervention Time Series Analysis of Crime Rates: The Impact of Sentence Reform in Virginia
  by Suncica Vujic & Jacques Commandeur & Siem Jan Koopman
• Feasible Invertibility Conditions and Maximum Likelihood Estimation for Observation-Driven Models
  by Francisco Blasques & Paolo Gorgi & Siem Jan Koopman & Olivier Wintenberger
• Exact Score for Time Series Models in State Space Form (Now published in Biometrika (1992), 79, 4, pp.283-6.)
  by Siem Jan Koopman & N. G. Shephard
• Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models
  by F. Blasques & P. Gorgi & S. Koopman & O. Wintenberger
• Fast Efficient Importance Sampling by State Space Methods
  by Siem Jan Koopman & Rutger Lit & Thuy Minh Nguyen
• Extracting Business Cycles using Semi-parametric Time-varying Spectra with Applications to US Macroeconomic Time Series
  by Siem Jan Koopman & Soon Yip Wong
• Stationarity and Ergodicity of Univariate Generalized Autoregressive Score Processes
  by Francisco Blasques & Siem Jan Koopman & Andre Lucas
• Statistical Software for State Space Methods
  by Commandeur, Jacques J. F. & Koopman, Siem Jan & Ooms, Marius
• Stock Index Volatility Forecasting with High Frequency Data
  by Eugenie Hol & Siem Jan Koopman
• Forecasting Daily Time Series using Periodic Unobserved Components Time Series Models
  by Siem Jan Koopman & Marius Ooms
• The Stochastic Volatility in Mean Model
  by Siem Jan Koopman & Eugenie Hol Uspensky
• A Dynamic Yield Curve Model with Stochastic Volatility and Non-Gaussian Interactions: An Empirical Study of Non-standard Monetary Policy in the Euro Area
  by Geert Mesters & Bernd Schwaab & Siem Jan Koopman
• Empirical Bayes Methods for Dynamic Factor Models
  by Siem Jan Koopman & Geert Mesters
• A Dynamic Bivariate Poisson Model for Analysing and Forecasting Match Results in the English Premier League
  by Siem Jan Koopman & Rutger Lit
• The Dynamic Skellam Model with Applications
  by Siem Jan Koopman & Rutger Lit & André Lucas
• The Effect of the Great Moderation on the U.S. Business Cycle in a Time-varying Multivariate Trend-cycle Model
  by Drew Creal & Siem Jan Koopman & Eric Zivot
• A Forty Year Assessment of Forecasting the Boat Race
  by Geert Mesters & Siem Jan Koopman
• Estimating Systematic Continuous-time Trends in Recidivism using a Non-Gaussian Panel Data Model
  by Siem Jan Koopman & André Lucas & Marius Ooms & Kees van Montfort & Victor van der Geest
• Dynamic Factor Models with Smooth Loadings for Analyzing the Term Structure of Interest Rates
  by Borus Jungbacker & Siem Jan Koopman & Michel van der Wel
• Dynamic Factor Analysis in The Presence of Missing Data
  by B. Jungbacker & S. J. Koopman & M. van der Wel
• Bayesian Dynamic Modeling of High-Frequency Integer Price Changes
  by Istvan Barra & Siem Jan Koopman & Agnieszka Borowska
• A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations
  by Drew Creal & Siem Jan Koopman & André Lucas
• A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League
  by Siem Jan Koopman & Rutger Lit
• Credit Cycles and Macro Fundamentals
  by Siem Jan Koopman & Roman Kraeussl & Andre Lucas & Andre Monteiro
• Business and Default Cycles for Credit Risk
  by Siem Jan Koopman & André Lucas
• Analyzing the Term Structure of Interest Rates using the Dynamic Nelson-Siegel Model with Time-Varying Parameters
  by Siem Jan Koopman & Max I. P. Mallee & Michel van der Wel
• A General Framework for Observation Driven Time-Varying Parameter Models
  by Drew Creal & Siem Jan Koopman & André Lucas
• A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk
  by Siem Jan Koopman & André Lucas & Robert Daniels
• A Note on “Continuous Invertibility and Stable QML Estimation of the EGARCH(1,1) Model”
  by Francisco Blasques & Paolo Gorgi & Siem Jan Koopman & Olivier Wintenberger
• An Hourly Periodic State Space Model for Modelling French National Electricity Load
  by V. Dordonnat & S. J. Koopman & M. Ooms & A. Dessertaine & J. Collet
• The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures
  by Siem Jan Koopman & Marcel Scharth
• Convergence in European GDP Series
  by Rob Luginbuhl & Siem Jan Koopman
• Modelling bid-ask spreads in competitive dealership markets
  by Koopman, S. J. M. & Lai, H. N.
• Signal Extraction and the Formulation of Unobserved Components Models
  by Harvey, A. C. & Koopman, S. J. M.
• Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives
  by Durbin, J. & Koopman, S. J. M.
• Fast Filtering and Smoothing for Multivariate State Space Models
  by Koopman, S. J. M. & Durbin, J.
• Statistical Algorithms for Models in State Space Using SsfPack 2.2
  by Koopman, S. J. M. & Shephard, N. & Doornik, J. A.
• Dynamic term structure models with score-driven time-varying parameters: estimation and forecasting
  by Siem Koopman & André Lucas & Marcin Zamojski
• Testing for Parameter Instability across Different Modeling Frameworks
  by Francesco Calvori & Drew Creal & Siem Jan Koopman & André Lucas
• Regime switches in the volatility and correlation of financial institutions
  by Kris Boudt & Jon Danielsson & Siem Jan Koopman & Andre Lucas
• The Dynamic Factor Network Model with an Application to Global Credit-Risk
  by Falk Bräuning & Siem Jan Koopman
• Modeling the business and financial cycle in a multivariate structural time series model
  by Jasper de Winter & Siem Jan Koopman & Irma Hindrayanto & Anjali Chouhan
• Joint Bayesian Analysis of Parameters and States in Nonlinear non‐Gaussian State Space Models
  by István Barra & Lennart Hoogerheide & Siem Jan Koopman & André Lucas
• Empirical Bayes Methods for Dynamic Factor Models
  by S. J. Koopman & G. Mesters
• Global Credit Risk: World, Country and Industry Factors
  by Bernd Schwaab & Siem Jan Koopman & André Lucas
• The dynamic factor network model with an application to global credit risk
  by Falk Bräuning & Siem Jan Koopman
• Bayesian Dynamic Modeling of High-Frequency Integer Price Changes
  by István Barra & Agnieszka Borowska & Siem Jan Koopman
• Time-Varying Transition Probabilities for Markov Regime Switching Models
  by Marco Bazzi & Francisco Blasques & Siem Jan Koopman & Andre Lucas
• Intraday Stochastic Volatility in Discrete Price Changes: The Dynamic Skellam Model
  by Siem Jan Koopman & Rutger Lit & André Lucas
• Missing Observations in Observation-Driven Time Series Models
  by Francisco (F. ) Blasques & Paolo Gorgi & Siem Jan (S. J. ) Koopman
• Unobserved Components with Stochastic Volatility in U.S. Inflation: Estimation and Signal Extraction
  by Mengheng Li & Siem Jan (S. J. ) Koopman
• The analysis and forecasting of ATP tennis matches using a high-dimensional dynamic model
  by P. Gorgi & Siem Jan (S. J. ) Koopman & R. Lit
• Forecasting economic time series using score-driven dynamic models with mixed-data sampling
  by Paolo Gorgi & Siem Jan (S. J. ) Koopman & Mengheng Li
• Accelerating GARCH and Score-Driven Models: Optimality, Estimation and Forecasting
  by Francisco (F. ) Blasques & Paolo Gorgi & Siem Jan (S. J. ) Koopman
• Forecasting Football Match Results in National League Competitions Using Score-Driven Time Series Models
  by Siem Jan (S. J. ) Koopman & Rutger Lit
• Fast Filtering and Smoothing for Multivariate State Space Models
  by S. J. Koopman & J. Durbin
• The stochastic volatility in mean model: empirical evidence from international stock markets
  by Siem Jan Koopman & Eugenie Hol Uspensky
• Amendments and Corrections
  by F. Blasques & S. J. Koopman & A. Lucas
• Realized Wishart-GARCH: A Score-driven Multi-Asset Volatility Model
  by P. Gorgi & P. R. Hansen & P. Janus & S. J. Koopman
• Bayesian Risk Forecasting for Long Horizons
  by Agnieszka Borowska & Lennart Hoogerheide & Siem Jan Koopman
• Dynamic discrete copula models for high‐frequency stock price changes
  by Siem Jan Koopman & Rutger Lit & André Lucas & Anne Opschoor
• Modified efficient importance sampling for partially non‐Gaussian state space models
  by Siem Jan Koopman & Rutger Lit & Thuy Minh Nguyen
• Forecasting football match results in national league competitions using score-driven time series models
  by Koopman, Siem Jan & Lit, Rutger
• A Time-Varying Parameter Model for Local Explosions
  by Francisco (F. ) Blasques & Siem Jan (S. J. ) Koopman & Marc Nientker
• Partially Censored Posterior for Robust and Efficient Risk Evaluation
  by Agnieszka Borowska & Lennart Hoogerheide & Siem Jan Koopman & Herman van Dijk
• Long-term forecasting of El Niño events via dynamic factor simulations
  by Li, Mengheng & Koopman, Siem Jan & Lit, Rutger & Petrova, Desislava
• Credit Cycles and Macro Fundamentals
  by Siem Jan Koopman & Roman Kräussl & André Lucas & André Monteiro
• Constructing Seasonally Adjusted Data with Time‐varying Confidence Intervals
  by Siem Jan Koopman & Philip Hans Franses
• Forecasting in a changing world: from the great recession to the COVID-19 pandemic
  by Mariia Artemova & Francisco Blasques & Siem Jan Koopman & Zhaokun Zhang
• The analysis and forecasting of tennis matches by using a high dimensional dynamic model
  by P. Gorgi & S. J. Koopman & R. Lit
• A statistical model of the global carbon budget
  by Mikkel Bennedsen & Eric Hillebrand & Siem Jan Koopman
• Partially Censored Posterior for robust and efficient risk evaluation
  by Agnieszka Borowska & Lennart Hoogerheide & Siem Jan Koopman & Herman K. van Dijk
• Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects
  by Paolo Gorgi & Siem Jan Koopman
• Partially censored posterior for robust and efficient risk evaluation
  by Borowska, Agnieszka & Hoogerheide, Lennart & Koopman, Siem Jan & van Dijk, Herman K.
• Nonlinear autoregressive models with optimality properties
  by Francisco Blasques & Siem Jan Koopman & André Lucas
• Modeling, Forecasting, and Nowcasting U.S. CO2 Emissions Using Many Macroeconomic Predictors
  by Mikkel Bennedsen & Eric Hillebrand & Siem Jan Koopman
• Forecasting economic time series using score-driven dynamic models with mixed-data sampling
  by Gorgi, Paolo & Koopman, Siem Jan & Li, Mengheng
• The dynamic factor network model with an application to international trade
  by Bräuning, Falk & Koopman, Siem Jan
• Dynamic Factor Models with Clustered Loadings: Forecasting Education Flows using Unemployment Data
  by Francisco Blasques & Meindert Heres Hoogerkamp & Siem Jan Koopman & Ilka van de Werve
• Accelerating score-driven time series models
  by Blasques, F. & Gorgi, P. & Koopman, S. J.
• Estimation of final standings in football competitions with premature ending: the case of COVID-19
  by Paolo Gorgi & Siem Jan Koopman & Rutger Lit
• Statistical Algorithms for Models in State Space Using SsfPack 2.2
  by Koopman, S. J. M. & Shephard, N. & Doornik, J. A.
• Fast Filtering and Smoothing for Multivariate State Space Models
  by Koopman, S. J. M. & Durbin, J.
• Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives
  by Durbin, J. & Koopman, S. J. M.
• Modelling bid-ask spreads in competitive dealership markets
  by Koopman, S. J. M. & Lai, H. N.
• Signal Extraction and the Formulation of Unobserved Components Models
  by Harvey, A. C. & Koopman, S. J. M.

editor of:

• State Space and Unobserved Component Models
  edited by Harvey, Andrew & Koopman, Siem Jan & Shephard, Neil
• State Space and Unobserved Component Models
  edited by Harvey, Andrew & Koopman, Siem Jan & Shephard, Neil
• Unobserved Components and Time Series Econometrics
  edited by Koopman, Siem Jan & Shephard, Neil
• Dynamic Factor Models
  edited by Eric Hillebrand & Siem Jan Koopman