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Economics and Finance

D-Index
65
Citations
24743
World Ranking
592
National Ranking
16

Overview

Siem Jan Koopman is affiliated with Vrije Universiteit Amsterdam in the Netherlands. Their research primarily focuses on Economics, Econometrics, and Finance, with a significant portion of work dedicated to subfields such as Economics and Econometrics, Finance, General Economics, Econometrics and Finance, Global and Planetary Change, and Atmospheric Science.

The main topics explored in Koopman's research include Monetary Policy and Economic Impact, Financial Risk and Volatility Modeling, Market Dynamics and Volatility, Complex Systems and Time Series Analysis, Atmospheric and Environmental Gas Dynamics, Stochastic Processes and Financial Applications, and Climate Change Policy and Economics.

Koopman has been published frequently in several venues, notably:

  • SSRN Electronic Journal
  • Journal of Econometrics
  • arXiv (Cornell University)
  • Journal of the Royal Statistical Society Series A (Statistics in Society)
  • Oxford Bulletin of Economics and Statistics

Among recent papers authored or co-authored by Koopman are:

  • "Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors," 2021, Energy Economics
  • "Maximum likelihood estimation for score-driven models," 2021, Journal of Econometrics
  • "A multivariate dynamic statistical model of the global carbon budget 1959-2020," 2023, Journal of the Royal Statistical Society Series A (Statistics in Society)
  • "Dynamic factor models with clustered loadings: Forecasting education flows using unemployment data," 2021, Data Archiving and Networked Services (DANS)
  • "Using rapid damage observations for Bayesian updating of hurricane vulnerability functions: A case study of Hurricane Dorian using social media," 2022, International Journal of Disaster Risk Reduction

Koopman's frequent co-authors include:

  • Francisco Blasques
  • Paolo Gorgi
  • Mikkel Bennedsen
  • Eric Hillebrand
  • J. van Brummelen

Best Publications

  • Time Series analysis by state space methods

    James Durbin;Siem Jan Koopman

  • GENERALIZED AUTOREGRESSIVE SCORE MODELS WITH APPLICATIONS

    Drew Creal;Siem Jan Koopman;Siem Jan Koopman;André Lucas;André Lucas

  • STAMP 6.0 Structural Time Series Analyser, Modeller and Predictor

    S.J. Koopman;A.C. Harvey;J.A. Doornik;N. Shephard

  • A Simple and Efficient Simulation Smoother for State Space Time Series Analysis

    J. Durbin;S. J. Koopman

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

    Siem Jan Koopman;Siem Jan Koopman;Borus Jungbacker;Eugenie Hol

  • Statistical algorithms for models in state space using SsfPack 2.2

    Siem Jan Koopman;Neil Shephard;Jurgen A. Doornik

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

    J. Durbin;S.J.M. Koopman

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

    J. Durbin;S.J.M. Koopman

  • An Introduction to State Space Time Series Analysis

    Jacques J.F. Commandeur;Siem Jan Koopman

  • Estimation of stochastic volatility models via Monte Carlo maximum likelihood

    Gleb Sandmann;Siem Jan Koopman

  • A dynamic multivariate heavy-tailed model for time-varying volatilities and correlations ⁄

    Drew Creal;Siem Jan Koopman;André Lucas

  • Periodic seasonal reg-ARFIMA-GARCH models for daily electricity spot prices

    Siem Jan Koopman;Marius Ooms;M. Angeles Carnero

  • Diagnostic Checking of Unobserved- Components Time Series Models

    Andrew C. Harvey;Siem Jan Koopman

  • Forecasting Hourly Electricity Demand Using Time-Varying Splines

    Andrew Harvey;Siem Jan Koopman

  • Exact Initial Kalman Filtering and Smoothing for Nonstationary Time Series Models

    Siem Jan Koopman

  • Disturbance smoother for state space models

    Siem Jan Koopman

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

    Siem Jan Koopman;Andrew C. Harvey;Jurgen A. Doornik;Neil Shephard

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

    Siem Jan Koopman;Eugenie Hol Uspensky

  • Credit Cycles and Macro Fundamentals

    Siem Jan Koopman;Siem Jan Koopman;Roman Kräussl;André Lucas;André Lucas;André B. Monteiro

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

    S.J. Koopman;M.I.P. Mallee;M. van der Wel

  • Time Series Analysis by State Space Methods: Second Edition

    Siem Jan Koopman;James Durbin

Frequent Co-Authors

Andre Lucas
Andre Lucas Vrije Universiteit Amsterdam
Andrew Harvey
Andrew Harvey University of Cambridge
Neil Shephard
Neil Shephard Harvard University
Herman K. van Dijk
Herman K. van Dijk Erasmus University Rotterdam
Peter Reinhard Hansen
Peter Reinhard Hansen University of North Carolina at Chapel Hill
Gary Koop
Gary Koop University of Strathclyde
Monica Billio
Monica Billio Ca Foscari University of Venice
Jean-Michel Zakoian
Jean-Michel Zakoian École Nationale de la Statistique et de l'Administration Économique
Stefan Mittnik
Stefan Mittnik Ludwig-Maximilians-Universität München
Olivier Scaillet
Olivier Scaillet University of Geneva

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