Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/102702
Authors: 
Warne, Anders
Coenen, Günter
Christoffel, Kai
Year of Publication: 
2014
Series/Report no.: 
CFS Working Paper Series 478
Abstract: 
The predictive likelihood is of particular relevance in a Bayesian setting when the purpose is to rank models in a forecast comparison exercise. This paper discusses how the predictive likelihood can be estimated for any subset of the observable variables in linear Gaussian state-space models with Bayesian methods, and proposes to utilize a missing observations consistent Kalman filter in the process of achieving this objective. As an empirical application, we analyze euro area data and compare the density forecast performance of a DSGE model to DSGE-VARs and reduced-form linear Gaussian models.
Subjects: 
Bayesian inference
density forecasting
Kalman filter
missing data
Monte Carlo integration
predictive likelihood
JEL: 
C11
C32
C52
C53
E37
Document Type: 
Working Paper

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