TY  - UNPD
A1  - Warne, Anders
A1  - Coenen, Günter
A1  - Christoffel, Kai
T1  - Marginalized predictive likelihood comparisons of linear Gaussian state-space models with applications to DSGE, DSGE-VAR, and VAR models
T2  - Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 478
N2  - he 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.
T3  - CFS working paper series - 478 
KW  - Bayesian inference
KW  - density forecasting
KW  - Kalman filter
KW  - missing data
KW  - Monte Carlo integration
KW  - predictive likelihood
Y1  - 2014
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/35110
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-351109
UR  - http://ssrn.com/abstract=2507827
IS  - June 27, 2014
PB  - Center for Financial Studies
CY  - Frankfurt, M.
ER  -