What is he best known for?
The fields of study he is best known for:
- Statistics
- Econometrics
- Regression analysis
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.
His most cited work include:
- Time Series Analysis by State Space Methods (1090 citations)
- Time Series analysis by state space methods (1005 citations)
- GENERALIZED AUTOREGRESSIVE SCORE MODELS WITH APPLICATIONS (434 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Econometrics (121.65%)
- Series (43.87%)
- Time series (34.49%)
What were the highlights of his more recent work (between 2015-2021)?
- Econometrics (121.65%)
- Series (43.87%)
- Importance sampling (30.88%)
In recent papers he was focusing on the following fields of study:
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
- Finance which connect with House price and Credit cycle,
- Time series, which have a strong connection to Kalman filter,
- Ordered probit most often made with reference to Variables.
Between 2015 and 2021, his most popular works were:
- PREDICTING TIME-VARYING PARAMETERS WITH PARAMETER-DRIVEN AND OBSERVATION-DRIVEN MODELS (55 citations)
- Spillover dynamics for systemic risk measurement using spatial financial time series models (39 citations)
- Spillover dynamics for systemic risk measurement using spatial financial time series models (39 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Algebra
- Regression analysis
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.
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