S. Koopman

发表

Maximum Likelihood Estimation for Generalized Autoregressive Score Models

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

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

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

Long Memory Dynamics for Multivariate Dependence Under Heavy Tails

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

Generalized autoregressive score models with applications ∗

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

Disturbance smoother for state space models

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

Forecasting Hourly Electricity Demand Using Time-Varying Splines

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

Linear state space models

J. Durbin, S. Koopman, 2012 .

Testing the assumptions behind importance sampling

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

Time Series Analysis by State Space Methods

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

Exact Initial Kalman Filtering and Smoothing for Nonstationary Time Series Models

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

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 .

econstor Make Your Publications Visible . A Service of zbw

S. Koopman, A. Lucas, A. Heinemann, 2005 .

Empirical Bayes Methods for Dynamic Factor Models

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

Empirical Bayes Methods for Dynamic Factor Models

S. Koopman, G. Mesters, 2014 .

Likelihood-based Analysis for Dynamic Factor Models

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

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

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

SMOOTH DYNAMIC FACTOR ANALYSIS WITH APPLICATION TO THE US TERM STRUCTURE OF INTEREST RATES: SMOOTH DYNAMIC FACTOR ANALYSIS

S. Koopman, Borus Jungbacker, M. V. Wel, 2014 .

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 .

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 .

Monte Carlo estimation for nonlinear non-Gaussian state space models

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

Measuring Asymmetric Stochastic Cycle Components

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

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

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

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

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

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

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

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

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

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

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

Exact score for time series models in state space form

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

Multivariate structural time series models

A. Harvey, S. Koopman, 1997 .

Time Series Modelling of Daily Tax Revenues

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

Likelihood functions for state space models with diffuse initial conditions

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

The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures

Siem Jan Koopman, Marcel Scharth, S. Koopman, 2011 .

Long Memory Modelling of Inflation with Stochastic Variance and Structural Breaks

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

Monte Carlo Likelihood Estimation for Three Multivariate Stochastic Volatility Models

Siem Jan Koopman, Borus Jungbacker, S. Koopman, 2006 .

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

Siem Jan Koopman, Borus Jungbacker, S. Koopman, 2005 .

State Space Models With a Common Stochastic Variance

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

Stock Index Volatility Forecasting with High Frequency Data

S. Koopman, Eugenie Hol Uspensky, Siem Jan Koopman, 2002 .

Testing the Assumptions Behind the use of Importance Sampling

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

Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series

Charles S. Bos, S. Koopman, 2002 .

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

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

Estimation of stochastic volatility models via Monte Carlo maximum likelihood

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

Tests for cycles

N. Shephard, A. Harvey, S. Koopman, 2004 .

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

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

Generalized autoregressive Method of Moments

Drew D. Creal, S. Koopman, A. Lucas, 2015 .

Periodic Unobserved Component Time Series Models : estimation and forecasting with applications

Siem Jan Koopman, Marius Ooms, S. Koopman, 2002 .

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 .

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

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

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

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

Forecasting Macroeconomic Variables Using Collapsed Dynamic Factor Analysis

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

Spline Smoothing Over Difficult Regions

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

Detecting shocks: Outliers and breaks in time series

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

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.

Forecasting and nowcasting economic growth in the euro area using factor models

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

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

S. Koopman, Irma Hindrayanto, Jasper de Winter, 2014 .

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

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

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

Drew D. Creal, Eric Zivot, S. Koopman, 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 .

Koopman , Siem Jan ; Ooms , Marius Working Paper Time Series Modelling of Daily Tax Revenues

S. Koopman, 2001 .

Long-term forecasting of El Niño events via dynamic factor simulations

S. Koopman, D. Petrova, R. Lit, 2020, Journal of Econometrics.

Testing for Parameter Instability across Different Modeling Frameworks

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

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

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

Testing for Parameter Instability across Competing Modeling Frameworks ∗

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

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

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

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

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

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

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

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

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

Signal extraction and the formulation of unobserved components models

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

Bayesian Risk Forecasting for Long Horizons

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

Structural Time Series Models

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

Bayesian Dynamic Modeling of High-Frequency Integer Price Changes

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

Dynamic Negative Binomial Di erence Model for High Frequency Returns ∗ PRELIMINARY AND INCOMPLETE

S. Koopman, I. Barra, 2015 .

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

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

Autoregressive Models with Time-Varying Dependence and Stochastic Volatility: An Application to Inflation Forecasting

S. Koopman, D. E. Turatti, 2017 .

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

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

The Analysis and Forecasting of ATP Tennis Matches Using a High-Dimensional Dynamic Model

S. Koopman, R. Lit, P. Gorgi, 2018 .

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

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

Forecasting the Final Ranking in Competitions with a Premature Ending due to COVID-19

S. Koopman, R. Lit, P. Gorgi, 2020 .

Constructing Seasonally Adjusted Data with Time-Varying Confidence Intervals

P. Franses, S. Koopman, 2001 .

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

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

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

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

The Information in Systemic Risk Rankings

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

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

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

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

S. Koopman, A. Lucas, A. Monteiro, 2005 .

Special Issue on Nonlinear Modelling and Financial Econometrics

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

Regime Switches in the Volatility and Correlation of Financial Institutions

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

Missing Observations in Observation-Driven Time Series Models

F. Blasques, S. Koopman, P. Gorgi, 2018, Journal of Econometrics.

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

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

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

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

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

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

Tracking Growth and the Business Cycle: A Stochastic Common Cycle Model for the Euro Area

S. Koopman, A. Rúa, João Valle e Azevedo, 2003 .

State Space and Unobserved Component Models: Empirical Bayesian inference in a nonparametric regression model

N. Shephard, A. Harvey, S. Koopman, 2004 .

Measuring Synchronisation and Convergence of Business Cycles

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

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

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

State Space Methods for Latent Trajectory and Parameter Estimation by Maximum Likelihood

S. Koopman, K. Montfort, J. Commandeur, 2010 .

Forecasting Cross-Sections of Frailty-Correlated Default

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

Periodic Unobserved Cycles in Seasonal Time Series: Identification and Estimation

S. Koopman, Irma Hindrayanto, M. Ooms, 2006 .

Toward X13 ?

J. Aston, S. Koopman, B. Monsell, 2003 .

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

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

Tracking the Business Cycle of the Euro Area

Siem Jan Koopman, António Rua, S. Koopman, 2006 .

Is there evidence of a trend in the CO2 airborne fraction?

S. Koopman, Eric Hillebrand, Mikkel Bennedsen, 2022, 2204.11651.

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

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

Exponentionally weighted methods for forecasting intraday time series with multiple seasonal cycles: Comments

S. Koopman, M. Ooms, 2010 .

CFEnetwork: The annals of computational and financial econometrics, 3rd issue

Robert Taylor, Jean-Marie Dufour, Helmut Lütkepohl, 2014, Computational Statistics & Data Analysis.

Vector Autoregressions with Dynamic Factor Coefficients and Conditionally Heteroskedastic Errors

S. Koopman, P. Gorgi, Julia Schaumburg, 2021 .

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

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

Unobserved components with stochastic volatility: Simulation‐based estimation and signal extraction

S. Koopman, Mengheng Li, 2021 .

Continuous Time State Space Modelling with an Application to High-Frequency Road Traffic Data

Siem Jan Koopman, Jacques J. F. Commandeur, Frits Bijleveld, 2018 .

Time Series: State Space Methods

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

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..

Testing for Parameter Instability in Competing Modeling Frameworks

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

Introduction to Local Level Model and Kalman Filter

S. J. Koopman, S. Koopman, 2011 .

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

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

Multivariate non‐linear time series modelling of exposure and risk in road safety research

Siem Jan Koopman, Kees van Montfort, Jacques J. F. Commandeur, 2010 .

Unobserved components models in economics and finance

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

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.

MESSY TIME SERIES: A UNIFIED APPROACH

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

Seasonality with trend and cycle interactions in unobserved components models

S. Koopman, Kai Ming Lee, 2008 .

Model‐based measurement of latent risk in time series with applications

Siem Jan Koopman, Jacques J. F. Commandeur, Frits Bijleveld, 2008 .

Extracting Business Cycles Using Semi-Parametric Time-Varying Spectra with Applications to Us Macroeconomic Time Series

S. Koopman, Soon Y. Wong, 2006 .

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

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

Empirical credit cycles and capital buffer formation

S. Koopman, A. Lucas, P. Klaassen, 2005 .

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

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

Interaction between structural and cyclical shocks in production and employment

S. Koopman, F. Butter, 2001 .

Computing Observation Weights for Signal Extraction and Filtering

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

Fast Filtering and Smoothing for Multivariate State Space Models

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

An Ox link to underlying C code

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

The modeling and seasonal adjustment of weekly observations

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

Interaction between Supply and Demand Shocks in Production and Employment

S. Koopman, F. Butter, 1997 .

Outliers and Switches in Time Series

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

Diagnostic Checking of Unobserved-Components Time Series Models

A. Harvey, S. Koopman, 1992 .

Diagnostic checking and intra-daily effects in time series models

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

Unobserved Components with Stochastic Volatility in U.S. Inflation: Estimation and Signal Extraction

S. Koopman, Mengheng Li, 2018 .

Global Credit Risk: World, Country and Industry Factors

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

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

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

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

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

Forecasting the Boat Race

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

Intervention Time Series Analysis of Crime Rates

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

Structural time series models in medicine

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

Kalman filtering and smoothing

S. Koopman, T. Colton, P. Armitage, 1998 .

Time Series Analysis by State Space Methods: Second Edition

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

A Statistical Model of the Global Carbon Budget

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

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

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

The dynamic factor network model with an application to international trade

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

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.

TI 11-042 / DSF 16 Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk

Andre Lucas, Drew D. Creal, Bernd Schwaab, 2011 .

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

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

Likelihood�?Based Dynamic Factor Analysis for Measurement and Forecasting

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

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..

Forecasting economic time series using unobserved components time series models

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

Maximum Likelihood Estimation for Dynamic Factor Models with Missing Data

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

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

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

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

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

Dynamic Factor Analysis in the Presence of Missing Data

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

Statistical algorithms for models in state space form: SsfPack 3.0

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

Statistical algorithms for models in state space using SsfPack 2.2

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

Stamp 5.0 : structural time series analyser, modeller and predictor

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

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

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

Information Theoretic Optimality of Observation Driven Time Series Models

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

Statistical Software for State Space Methods

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

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

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

Forecasting Interest Rates with Shifting Endpoints

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

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.

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

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

On the evidence of a trend in the CO 2 airborne fraction

S. Koopman, Eric Hillebrand, Mikkel Bennedsen, 2022, 2204.11651.

Is there evidence of a trend in the CO2 airborne fraction?

S. Koopman, Eric Hillebrand, Mikkel Bennedsen, 2022, 2204.11651.

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 .

Periodic Heteroskedastic Regarfima Models for Daily Electricity Spot Prices

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

Forecasting the Varibility of Stock Index Returns with Stochastic Volatility Models and Implied Volatility

S. Koopman, E. Hol, 2000 .

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

Vu, Siem Jan Koopman, Rutger Lit, 2012 .

Common and Idiosyncratic Conditional Volatility Factors: Theory and Empirical Evidence

F. Blasques, S. Koopman, Enzo D’Innocenzo, 2021 .

A Time-Varying Parameter Model for Local Explosions

F. Blasques, S. Koopman, Marc Nientker, 2018, Journal of Econometrics.

Maximum Likelihood Estimation for Score-Driven Models

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

Conditional Score Residuals and Diagnostic Analysis of Serial Dependence in Time Series Models

F. Blasques, S. Koopman, P. Gorgi, 2021, SSRN Electronic Journal.

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

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

Maximum Likelihood Estimation for Score-Driven Time Series Models

F. Blasques, S. Koopman, A. Lucas, 2020 .

Nonlinear autoregressive models with optimality properties

F. Blasques, S. Koopman, A. Lucas, 2020 .

Time‐Varying Transition Probabilities for Markov Regime Switching Models

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

Maximum Likelihood Estimation for Score-Driven Models REVISION : 23 October 2017

F. Blasques, S. Koopman, A. Lucas, 2017 .

Web Appendix to : “ Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time Series Models ”

F. Blasques, S. Koopman, A. Lucas, 2016 .

A Note on 'Continuous Invertibility and Stable QML Estimation of the EGARCH (1,1) Model'

F. Blasques, S. Koopman, O. Wintenberger, 2015, SSRN Electronic Journal.

Weighted Maximum Likelihood Estimator for Mixed Frequency Dynamic Factor Models

F. Blasques, S. Koopman, Max I. P. Mallee, 2015 .

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

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

Low Frequency and Weighted Likelihood Solutions for Mixed Frequency Dynamic Factor Models

Siem Jan Koopman, Francisco Blasques, Max Mallee, 2014 .

Optimal Formulations for Nonlinear Autoregressive Processes

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

Maximum Likelihood Estimation for correctly Specified Generalized Autoregressive Score Models: Feedback Effects, Contraction Conditions and Asymptotic Properties

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

Time Varying Transition Probabilities for Markov Regime Switching Models

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

Stationarity and Ergodicity of Univariate Generalized Autoregressive Score Processes

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

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 .

Systemic Risk Diagnostics: Coincident Indicators and Early Warning Signals

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

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 .

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

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

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

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

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.

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

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

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

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

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

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

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 .

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 .

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, 2008 .

Parameter Estimation and Practical Aspects of Modeling Stochastic Volatility

Siem Jan Koopman, Borus Jungbacker, S. Koopman, 2009 .

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 .

Joint Independent Metropolis-Hastings Methods for Nonlinear Non-Gaussian State Space Models

Lennart F. Hoogerheide, S. Koopman, A. Lucas, 2012 .

Dynamic Models for Policy, Economics and Society: Time Series Methods ICPSR Summer Program in Quantitative Methods of Social Research July 21st-July 25th, 2014

S. Koopman, S. Hall, H. Clarke, 2014 .

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

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

Dynamic discrete copula models for high‐frequency stock price changes

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

Discrete Copula Distributions with Time-Varying Marginals and Dependence Structure ∗

S. Koopman, A. Lucas, R. Lit, 2015 .

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 .

The Dynamic Nelson-Siegel Model with Time-Varying Loadings and Volatility

S. Koopman, Max I. P. Mallee, M. V. D. Wel, 2007 .

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.

An Introduction to State Space Time Series Analysis

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

Business and Default Cycles for Credit Risk

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

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

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

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.

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

S. Koopman, Irma Hindrayanto, Jasper de Winter, 2014 .

Structural Time Series Models

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

Credit Cycles and Macro Fundamentals

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

Convergence in European GDP series: a multivariate common converging trend–cycle decomposition

S. Koopman, R. Luginbuhl, 2004 .

Convergence in European GDP Series

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

Kalman filtering and smoothing for model-based signal extraction that depend on time-varying spectra

Siem Jan Koopman, Soon Yip Wong, S. Koopman, 2011 .

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

Siem Jan Koopman, Rashmin Gunasekera, Antonios Pomonis, 2020 .

Systematic risk diagnostics

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

Nowcasting and forecasting global financial sector stress and credit market dislocation

S. Koopman, A. Lucas, B. Schwaab, 2014 .

Global, Regional and Country Factors for the World Economy: a dynamic factor approach

S. Koopman, S. Grassi, Borus Jungbacker, 2009 .

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 .

Modeling Frailty-Correlated Defaults Using Many Macroeconomic Covariates

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

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

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