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Erscheinungsjahr: 
2008
Schriftenreihe/Nr.: 
Tinbergen Institute Discussion Paper No. 08-007/4
Verlag: 
Tinbergen Institute, Amsterdam and Rotterdam
Zusammenfassung: 
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.
Schlagwörter: 
EM algorithm
Kalman Filter
Forecasting
Latent Factors
Markov chain Monte Carlo
Principal Components
State Space
JEL: 
C33
C43
Dokumentart: 
Working Paper
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