Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/86765
Authors: 
Jungbacker, Borus
Koopman, Siem Jan
Year of Publication: 
2008
Series/Report no.: 
Tinbergen Institute Discussion Paper 08-007/4
Abstract: 
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 correlated innovations. The new results lead to computationally efficient procedures for the estimation of the factors and parameter estimation by maximum likelihood and Bayesian methods. An illustration is provided for the analysis of a large panel of macroeconomic time series.
Subjects: 
EM algorithm
Kalman Filter
Forecasting
Latent Factors
Markov chain Monte Carlo
Principal Components
State Space
JEL: 
C33
C43
Document Type: 
Working Paper

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