Siem Jan Koopman
Datos Biográficos
| ID | 5729145 |
|---|---|
| NOMBRE | Siem Jan Koopman |
| NOMBRES | Siem Jan |
| APELLIDO | Koopman |
| FIRMA | KOOPMAN S J |
| AFILIACIONES | Tinbergen Institute |
| ORCID | 0000-0002-4440-9524 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 23 |
| TOTAL DE CITAS | 0 |
| TOTAL COMO AUTOR | 22 |
| TOTAL COMO EDITOR | 1 |
| PRIMER AÑO DE PUBLICACIÓN | 1992 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2026 |
| ÍNDICE H | 0 |
Measuring Growth Spillovers
We propose a multilevel econometric model with time‐varying spillover parameters that disentangle within‐country from between‐country growth spillovers. Parameter estimation is carried out by the method of maximum likelihood. The finite‐sample properties of the resulting estimates are validated through a Monte Carlo study. We illustrate the model properties in an empirical application for six Latin American countries. The results show that our mu…
Extremum Monte Carlo Filters
We introduce a novel simulation-based method for signal extraction in a general class of state space models. It can be used to estimate time-varying conditional means, modes, and quantiles, and to predict latent variables or forecast observations. The method consists of generating artificial datasets from the model and estimating the quantities of interest via extremum estimation. The approach is broadly applicable and its implementation is strai…
Exploring the crime drop in European Union homicide rates using econometric modeling
In this study, we employ a newly developed time series econometric approach to investigate the development in crime rates in various members states of the European Union (EU) between 1968 and 2019. We propose a panel data model with stochastically time-varying factors that also includes country-specific effects. This model enables us to evaluate the existence of a common EU crime trend, including a crime drop, to describe how individual countries…
Conditional Score Residuals and Diagnostic Analysis of Serial Dependence in Time Series Models
This paper introduces conditional score residuals and proposes a general framework for the diagnostic analysis of time series models. Conditional score residuals encompass commonly used definitions of residuals in time series models, including ARMA residuals, squared residuals, and Pearson residuals. In particular, these residuals are special cases of conditional score residuals when the conditional distribution of the model belongs to the expone…
Using rapid damage observations for Bayesian updating of hurricane vulnerability functions
Rapid impact assessments immediately after disasters are crucial to enable rapid and effective mobilization of resources for response and recovery efforts. These assessments are often performed by analysing the three components of risk: hazard, exposure and vulnerability. Vulnerability curves are often constructed using historic insurance data or expert judgments, reducing their applicability for the characteristics of the specific hazard and bui…
Joint Decomposition of Business and Financial Cycles
We discuss a model‐based simultaneous decomposition of multiple time series in short‐term and medium‐term cyclical dynamics. We associate short‐term dynamic features with the business cycle and medium‐term dynamic features with the financial cycle. For eight advanced economies, we analyse a set of macroeconomic and financial time series data. A strong and common finding among all economies is the co‐cyclicality of medium‐term cycles, especially t…
Unobserved components with stochastic volatility
The unobserved components time series model with stochastic volatility has gained much interest in econometrics, especially for the purpose of modelling and forecasting inflation. We present a feasible simulated maximum likelihood method for parameter estimation from a classical perspective. The method can also be used for evaluating the marginal likelihood function in a Bayesian analysis. We show that our simulation‐based method is computational…
Empirical Bayes Methods for Dynamic Factor Models
We consider the dynamic factor model where the loading matrix, the dynamic factors, and the disturbances are treated as latent stochastic processes. We present empirical Bayes methods that enable the shrinkagebased estimation of the loadings and factors. We investigate the methods in a large Monte Carlo study where we evaluate the finite sample properties of the empirical Bayes methods for quadratic loss functions. Finally, we present and discuss…
Dynamic Factor Models
Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models
We verify whether parameter-driven and observation-driven classes of dynamic models can outperform each other in predicting time-varying parameters. We consider existing and new dynamic models for counts and durations, but also for volatility, intensity, and dependence parameters. In an extended Monte Carlo study, we present evidence that observation-driven models based on the score of the predictive likelihood function have similar predictive ac…
Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State-Space Models
We propose a general likelihood evaluation method for nonlinear non-Gaussian state-space models using the simulation-based method of efficient importance sampling. We minimize the simulation effort by replacing some key steps of the likelihood estimation procedure by numerical integration. We refer to this method as numerically accelerated importance sampling. We show that the likelihood function for models with a high-dimensional state vector an…
Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk
We propose an observation-driven dynamic factor model for mixed-measurement and mixed-frequency panel data. Time series observations may come from a range of families of distributions, be observed at different frequencies, have missing observations, and exhibit common dynamics and cross-sectional dependence due to shared dynamic latent factors. A feature of our model is that the likelihood function is known in closed form. This enables parameter …
Time Series Analysis by State Space Methods
This is a comprehensive treatment of the state space approach to time series analysis. A distinguishing feature of state space time series models is that observations are regarded as made up of distinct components, which are each modelled separately.
Dynamic Factor Models With Macro, Frailty, and Industry Effects for U.S. Default Counts
We develop a high-dimensional, nonlinear, and non-Gaussian dynamic factor model for the decomposition of systematic default risk conditions into latent components for (1) macroeconomic/financial risk, (2) autonomous default dynamics (frailty), and (3) industry-specific effects. We analyze discrete U.S. corporate default counts together with macroeconomic and financial variables in one unifying framework. We find that approximately 35% of default …
A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations
We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate Student t distribution. The key novelty of our proposed model concerns the weighting of lagged squared innovations for the estimation of future correlations…
Analyzing the Term Structure of Interest Rates Using the Dynamic Nelson–Siegel Model With Time-Varying Parameters
In this article we introduce time-varying parameters in the dynamic Nelson–Siegel yield curve model for the simultaneous analysis and forecasting of interest rates of different maturities. The Nelson–Siegel model has been recently reformulated as a dynamic factor model with vector autoregressive factors. We extend this framework in two directions. First, the factor loadings in the Nelson–Siegel yield model depend on a single loading parameter tha…
A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk
We model 1981–2005 quarterly default frequencies for a panel of U.S. firms in different rating and age classes from the Standard and Poor database. The data are decomposed into systematic and firm-specific risk components, where the systematic component reflects the general economic conditions and the default climate. We need to cope with: the shared exposure of each age cohort, industry, and rating class to the same systematic risk factor; stron…
Modeling Around-the-Clock Price Discovery for Cross-Listed Stocks Using State Space Methods
U.S. trading in non-U.S. stocks has grown dramatically. Around the clock, these stocks trade in the home market, in the U.S. market, and, potentially, in both markets simultaneously. We develop a general methodology based on a state space model to study 24-hour price discovery in a multiple-markets setting. As opposed to the standard variance ratio approach, this model deals naturally with (1) simultaneous quotes in an overlap, (2) missing observ…
Tracking the Business Cycle of the Euro Area
This article proposes a multivariate bandpass filter based on the trend plus cycle decomposition model. The underlying multivariate dynamic factor model relies on specific formulations for trend and cycle components and produces smooth business cycle indicators with bandpass filter properties. Furthermore, cycle shifts for individual time series are incorporated as part of the multivariate model and estimated simultaneously with the remaining par…
State Space Models With a Common Stochastic Variance
This article considers a combination of the linear Gaussian state space model and the stochastic volatility model. The focus is on the simultaneous estimation of parameters related to the stochastic processes of both the mean and variance parts of the model. Kalman filter and Monte Carlo maximum likelihood methods lead to an elegant estimation procedure for which the simulation error can be made arbitrarily small. The standard asymptotic properti…
The Modeling and Seasonal Adjustment of Weekly Observations
Several important economic time series are recorded on a particular day every week. Seasonal adjustment of such series is difficult because the number of weeks varies between 52 and 53 and the position of the recording day changes from year to year. In addition certain festivals, most notably Easter, take place at different times according to the year. This article presents a solution to problems of this kind by setting up a structural time serie…
Stamp 5.0 Structural Time Series Analyser, Modeller and Predictor
Part 1: installation procedure for STAMP. Part 2 Tutorials on structural time series modelling: getting started on simple univariate modelling tutorial on components tutorial on interventions and explanatory variables tutorial on multivariate models applications in macroeconomics and finance. Part 3 STAMP tutorials: the basic skills tutorial on graphics tutorial on data input and output tutorial on data transformation and description tutorial on …
Diagnostic Checking of Unobserved-Components Time Series Models
Diagnostic checking of the specification of time series models is normally carried out using the innovations—that is, the one-step-ahead prediction errors. In an unobserved-components model, other sets of residuals are available. These auxiliary residuals are estimators of the disturbances associated with the unobserved components. They can often yield information that is less apparent from the innovations, but they suffer from the disadvantage t…
Sin obras prominentes en esta página.
Diagnostic Checking of Unobserved-Components Time Series Models
Diagnostic checking of the specification of time series models is normally carried out using the innovations—that is, the one-step-ahead prediction errors. In an unobserved-components model, other sets of residuals are available. These auxiliary residuals are estimators of the disturbances associated with the unobserved components. They can often yield information that is less apparent from the innovations, but they suffer from the disadvantage t…
Stamp 5.0 Structural Time Series Analyser, Modeller and Predictor
Part 1: installation procedure for STAMP. Part 2 Tutorials on structural time series modelling: getting started on simple univariate modelling tutorial on components tutorial on interventions and explanatory variables tutorial on multivariate models applications in macroeconomics and finance. Part 3 STAMP tutorials: the basic skills tutorial on graphics tutorial on data input and output tutorial on data transformation and description tutorial on …
The Modeling and Seasonal Adjustment of Weekly Observations
Several important economic time series are recorded on a particular day every week. Seasonal adjustment of such series is difficult because the number of weeks varies between 52 and 53 and the position of the recording day changes from year to year. In addition certain festivals, most notably Easter, take place at different times according to the year. This article presents a solution to problems of this kind by setting up a structural time serie…
State Space Models With a Common Stochastic Variance
This article considers a combination of the linear Gaussian state space model and the stochastic volatility model. The focus is on the simultaneous estimation of parameters related to the stochastic processes of both the mean and variance parts of the model. Kalman filter and Monte Carlo maximum likelihood methods lead to an elegant estimation procedure for which the simulation error can be made arbitrarily small. The standard asymptotic properti…
Tracking the Business Cycle of the Euro Area
This article proposes a multivariate bandpass filter based on the trend plus cycle decomposition model. The underlying multivariate dynamic factor model relies on specific formulations for trend and cycle components and produces smooth business cycle indicators with bandpass filter properties. Furthermore, cycle shifts for individual time series are incorporated as part of the multivariate model and estimated simultaneously with the remaining par…
Modeling Around-the-Clock Price Discovery for Cross-Listed Stocks Using State Space Methods
U.S. trading in non-U.S. stocks has grown dramatically. Around the clock, these stocks trade in the home market, in the U.S. market, and, potentially, in both markets simultaneously. We develop a general methodology based on a state space model to study 24-hour price discovery in a multiple-markets setting. As opposed to the standard variance ratio approach, this model deals naturally with (1) simultaneous quotes in an overlap, (2) missing observ…
A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk
We model 1981–2005 quarterly default frequencies for a panel of U.S. firms in different rating and age classes from the Standard and Poor database. The data are decomposed into systematic and firm-specific risk components, where the systematic component reflects the general economic conditions and the default climate. We need to cope with: the shared exposure of each age cohort, industry, and rating class to the same systematic risk factor; stron…
Analyzing the Term Structure of Interest Rates Using the Dynamic Nelson–Siegel Model With Time-Varying Parameters
In this article we introduce time-varying parameters in the dynamic Nelson–Siegel yield curve model for the simultaneous analysis and forecasting of interest rates of different maturities. The Nelson–Siegel model has been recently reformulated as a dynamic factor model with vector autoregressive factors. We extend this framework in two directions. First, the factor loadings in the Nelson–Siegel yield model depend on a single loading parameter tha…
A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations
We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate Student t distribution. The key novelty of our proposed model concerns the weighting of lagged squared innovations for the estimation of future correlations…
Time Series Analysis by State Space Methods
This is a comprehensive treatment of the state space approach to time series analysis. A distinguishing feature of state space time series models is that observations are regarded as made up of distinct components, which are each modelled separately.
Dynamic Factor Models With Macro, Frailty, and Industry Effects for U.S. Default Counts
We develop a high-dimensional, nonlinear, and non-Gaussian dynamic factor model for the decomposition of systematic default risk conditions into latent components for (1) macroeconomic/financial risk, (2) autonomous default dynamics (frailty), and (3) industry-specific effects. We analyze discrete U.S. corporate default counts together with macroeconomic and financial variables in one unifying framework. We find that approximately 35% of default …
Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk
We propose an observation-driven dynamic factor model for mixed-measurement and mixed-frequency panel data. Time series observations may come from a range of families of distributions, be observed at different frequencies, have missing observations, and exhibit common dynamics and cross-sectional dependence due to shared dynamic latent factors. A feature of our model is that the likelihood function is known in closed form. This enables parameter …
Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State-Space Models
We propose a general likelihood evaluation method for nonlinear non-Gaussian state-space models using the simulation-based method of efficient importance sampling. We minimize the simulation effort by replacing some key steps of the likelihood estimation procedure by numerical integration. We refer to this method as numerically accelerated importance sampling. We show that the likelihood function for models with a high-dimensional state vector an…
Dynamic Factor Models
Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models
We verify whether parameter-driven and observation-driven classes of dynamic models can outperform each other in predicting time-varying parameters. We consider existing and new dynamic models for counts and durations, but also for volatility, intensity, and dependence parameters. In an extended Monte Carlo study, we present evidence that observation-driven models based on the score of the predictive likelihood function have similar predictive ac…
Empirical Bayes Methods for Dynamic Factor Models
We consider the dynamic factor model where the loading matrix, the dynamic factors, and the disturbances are treated as latent stochastic processes. We present empirical Bayes methods that enable the shrinkagebased estimation of the loadings and factors. We investigate the methods in a large Monte Carlo study where we evaluate the finite sample properties of the empirical Bayes methods for quadratic loss functions. Finally, we present and discuss…
Unobserved components with stochastic volatility
The unobserved components time series model with stochastic volatility has gained much interest in econometrics, especially for the purpose of modelling and forecasting inflation. We present a feasible simulated maximum likelihood method for parameter estimation from a classical perspective. The method can also be used for evaluating the marginal likelihood function in a Bayesian analysis. We show that our simulation‐based method is computational…
Using rapid damage observations for Bayesian updating of hurricane vulnerability functions
Rapid impact assessments immediately after disasters are crucial to enable rapid and effective mobilization of resources for response and recovery efforts. These assessments are often performed by analysing the three components of risk: hazard, exposure and vulnerability. Vulnerability curves are often constructed using historic insurance data or expert judgments, reducing their applicability for the characteristics of the specific hazard and bui…
Joint Decomposition of Business and Financial Cycles
We discuss a model‐based simultaneous decomposition of multiple time series in short‐term and medium‐term cyclical dynamics. We associate short‐term dynamic features with the business cycle and medium‐term dynamic features with the financial cycle. For eight advanced economies, we analyse a set of macroeconomic and financial time series data. A strong and common finding among all economies is the co‐cyclicality of medium‐term cycles, especially t…
Conditional Score Residuals and Diagnostic Analysis of Serial Dependence in Time Series Models
This paper introduces conditional score residuals and proposes a general framework for the diagnostic analysis of time series models. Conditional score residuals encompass commonly used definitions of residuals in time series models, including ARMA residuals, squared residuals, and Pearson residuals. In particular, these residuals are special cases of conditional score residuals when the conditional distribution of the model belongs to the expone…
Measuring Growth Spillovers
We propose a multilevel econometric model with time‐varying spillover parameters that disentangle within‐country from between‐country growth spillovers. Parameter estimation is carried out by the method of maximum likelihood. The finite‐sample properties of the resulting estimates are validated through a Monte Carlo study. We illustrate the model properties in an empirical application for six Latin American countries. The results show that our mu…
Extremum Monte Carlo Filters
We introduce a novel simulation-based method for signal extraction in a general class of state space models. It can be used to estimate time-varying conditional means, modes, and quantiles, and to predict latent variables or forecast observations. The method consists of generating artificial datasets from the model and estimating the quantities of interest via extremum estimation. The approach is broadly applicable and its implementation is strai…
Exploring the crime drop in European Union homicide rates using econometric modeling
In this study, we employ a newly developed time series econometric approach to investigate the development in crime rates in various members states of the European Union (EU) between 1968 and 2019. We propose a panel data model with stochastically time-varying factors that also includes country-specific effects. This model enables us to evaluate the existence of a common EU crime trend, including a crime drop, to describe how individual countries…
Econometrics (18 obras) · Computer Science (16 obras) · Mathematics (15 obras) · Statistics (15 obras) · Economics (12 obras) · Financial Risk and Volatility Modeling (9 obras) · Monetary Policy and Economic Impact (7 obras) · Algorithm (5 obras) · Dynamic factor (5 obras) · Monte Carlo method (5 obras)