Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/98863
Authors: 
Koopman, Siem Jan
Mesters, Geert
Year of Publication: 
2014
Series/Report no.: 
Tinbergen Institute Discussion Paper 14-061/III
Abstract: 
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 have lower quadratic loss compared to the standard maximum likelihood estimates. We investigate the methods in a Monte Carlo study where we document the finite sample properties. Finally, we present and discuss the results of an empirical study concerning the forecasting of U.S. macroeconomic time series using our empirical Bayes methods.
Subjects: 
Importance sampling
Kalman filtering
Likelihood-based analysis
Posterior modes
Rao-Blackwellization
Shrinkage
JEL: 
C32
C43
Document Type: 
Working Paper

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