Econometrics, State space, Statistics, Kalman filter and Smoothing are his primary areas of study. His work on Estimation theory expands to the thematically related Econometrics. His State space study incorporates themes from State vector, Time series, State-space representation, Applied mathematics and Algorithm.
His Statistics research is multidisciplinary, relying on both Business cycle and Systematic risk. His Kalman filter research includes elements of Mathematical optimization and Series. His Smoothing research includes themes of Calculus and Markov chain Monte Carlo.
Siem Jan Koopman mainly investigates Econometrics, Series, Time series, Kalman filter and Volatility. His research integrates issues of Statistics, Multivariate statistics and Importance sampling in his study of Econometrics. His work on Seasonal adjustment as part of general Series study is frequently linked to Component, bridging the gap between disciplines.
His work carried out in the field of Time series brings together such families of science as Business cycle, Actuarial science, State space, Maximum likelihood and Empirical research. His research investigates the connection between Kalman filter and topics such as Smoothing that intersect with problems in Algorithm. His study focuses on the intersection of Volatility and fields such as Monte Carlo method with connections in the field of Range.
His scientific interests lie mostly in Econometrics, Series, Importance sampling, Multivariate statistics and Volatility. His work in the fields of Dynamic factor overlaps with other areas such as Weighting. His Series study combines topics in areas such as Estimator and Consistency.
His Importance sampling research is multidisciplinary, incorporating perspectives in Estimation theory, Likelihood function, Algorithm and Bayesian probability, Bayesian inference. Siem Jan Koopman focuses mostly in the field of Bayesian inference, narrowing it down to topics relating to Stochastic volatility and, in certain cases, State space. His research on Multivariate statistics also deals with topics like
His primary scientific interests are in Econometrics, Multivariate statistics, Time series, Finance and Maximum likelihood. The concepts of his Econometrics study are interwoven with issues in Estimation theory, Gross domestic product and Systemic risk. Siem Jan Koopman has included themes like Kalman filter and Statistical model in his Estimation theory study.
His Multivariate statistics research is multidisciplinary, incorporating elements of Atmospheric sciences, Sink and Trend analysis. His research investigates the connection between Business cycle and topics such as Financial stability that intersect with issues in State space. His Monte Carlo method research incorporates elements of Volatility and Range.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Time Series analysis by state space methods
James Durbin;Siem Jan Koopman.
ACM Transactions on Spatial Algorithms and Systems (2012)
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.
Journal of Empirical Finance (2005)
Statistical algorithms for models in state space using SsfPack 2.2
Siem Jan Koopman;Neil Shephard;Jurgen A. Doornik.
Econometrics Journal (1999)
GENERALIZED AUTOREGRESSIVE SCORE MODELS WITH APPLICATIONS
Drew Creal;Siem Jan Koopman;Siem Jan Koopman;André Lucas;André Lucas.
Journal of Applied Econometrics (2013)
Monte Carlo maximum likelihood estimation for non-Gaussian state space models
J. Durbin;S.J.M. Koopman.
An Introduction to State Space Time Series Analysis
Jacques J. F. Commandeur;Siem Jan 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.
Journal of The Royal Statistical Society Series B-statistical Methodology (2000)
Estimation of stochastic volatility models via Monte Carlo maximum likelihood
Gleb Sandmann;Siem Jan Koopman.
Journal of Econometrics (1998)
Profile was last updated on December 6th, 2021.
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