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The effect of the great moderation on the US business cycle in a time-varying multivariate trend-cycle model
Creal, Drew; Koopman, Siem Jan; Zivot, Eric - 2008
accounts for time variation in macroeconomic volatility, known as the great moderation. In particular, we consider an … volatility processes and mixture distributions for the irregular components and the common cycle disturbances enable us to … that time-varying volatility is only present in the a selection of idiosyncratic components while the coefficients driving …
Persistent link: https://www.econbiz.de/10011376640
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Tracking Growth and the Business Cycle: a Stochastic Common Cycle Model for the Euro Area
e Azevedo, Joao Valle; Koopman, Siem Jan; Rua, Antonio - 2003
This paper proposes a new model-based method to obtain a coincident indicator for the business cycle. A dynamic factor model with trend components and a common cycle component is considered which can be estimated using standard maximum likelihood methods. The multivariate unobserved components...
Persistent link: https://www.econbiz.de/10010324815
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Tracking growth and the business cycle : a stochastic common cycle model for the Euro area
Azevedo, João Valle e; Koopman, Siem Jan; Rua, António - 2003
This paper proposes a new model-based method to obtain a coincident indicator for the business cycle. A dynamic factor model with trend components and a common cycle component is considered which can be estimated using standard maximum likelihood methods. The multivariate unobserved components...
Persistent link: https://www.econbiz.de/10011334364
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Tracking Growth and the Business Cycle : A Stochastic Common Cycle Model for the Euro Area
Valle e Azevedo, João; Koopman, Siem Jan; Rua, Antonio - 2003
This paper proposes a new model-based method to obtain a coincident indicator for the business cycle. A dynamic factor model with trend components and a common cycle component is considered which can be estimated using standard maximum likelihood methods. The multivariate unobserved components...
Persistent link: https://www.econbiz.de/10014075576
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Likelihood-based Analysis for Dynamic Factor Models
Jungbacker, Borus; Koopman, Siem Jan - 2008
We present new results for the likelihood-based analysis of the dynamic factor model that possibly includes intercepts and explanatory variables. The latent factors are modelled by stochastic processes. The idiosyncratic disturbances are specified as autoregressive processes with mutually...
Persistent link: https://www.econbiz.de/10010325750
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Likelihood-based analysis for dynamic factor models
Jungbacker, Borus; Koopman, Siem Jan - 2008
We present new results for the likelihood-based analysis of the dynamic factor model that possibly includes intercepts and explanatory variables. The latent factors are modelled by stochastic processes. The idiosyncratic disturbances are specified as autoregressive processes with mutually...
Persistent link: https://www.econbiz.de/10011373811
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Dynamic factor analysis in the presence of missing data
Jungbacker, Borus; Koopman, Siem Jan; Wel, Michel van der - 2011
This paper concerns estimating parameters in a high-dimensional dynamic factormodel by the method of maximum likelihood. To accommodate missing data in theanalysis, we propose a new model representation for the dynamic factor model. Itallows the Kalman filter and related smoothing methods to...
Persistent link: https://www.econbiz.de/10011377572
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Empirical Bayes methods for dynamic factor models
Koopman, Siem Jan; Mesters, Geert - 2014
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 efficient shrinkage-based estimation of the loadings and the factors. We show that our estimates...
Persistent link: https://www.econbiz.de/10010357912
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Maximum Likelihood Estimation for Dynamic Factor Models with Missing Data
Jungbacker, Borus - 2011
This paper concerns estimating parameters in a high-dimensional dynamic factor model by the method of maximum likelihood. To accommodate missing data in the analysis, we propose a new model representation for the dynamic factor model. It allows the Kalman filter and related smoothing methods to...
Persistent link: https://www.econbiz.de/10012756283
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Empirical Bayes Methods for Dynamic Factor Models
Koopman, Siem Jan - 2014
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 efficient shrinkage-based estimation of the loadings and the factors. We show that our estimates...
Persistent link: https://www.econbiz.de/10013053560