Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/102702 
Erscheinungsjahr: 
2014
Schriftenreihe/Nr.: 
CFS Working Paper Series No. 478
Verlag: 
Goethe University Frankfurt, Center for Financial Studies (CFS), Frankfurt a. M.
Zusammenfassung: 
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.
Schlagwörter: 
Bayesian inference
density forecasting
Kalman filter
missing data
Monte Carlo integration
predictive likelihood
JEL: 
C11
C32
C52
C53
E37
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
781.08 kB





Publikationen in EconStor sind urheberrechtlich geschützt.