Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/153969
Authors: 
Warne, Anders
Coenen, Günter
Christoffel, Kai
Year of Publication: 
2013
Series/Report no.: 
ECB Working Paper 1536
Abstract: 
This paper shows how to compute the h-step-ahead predictive likelihood for any subset of the observed variables in parametric discrete time series models estimated with Bayesian methods. The subset of variables may vary across forecast horizons and the problem thereby covers marginal and joint predictive likelihoods for a fixed subset as special cases. The basic idea is to utilize well-known techniques for handling missing data when computing the likelihood function, such as a missing observations consistent Kalman filter for linear Gaussian models, but it also extends to nonlinear, nonnormal state-space models. The predictive likelihood can thereafter be calculated via Monte Carlo integration using draws from the posterior distribution. As an empirical illustration, we use euro area data and compare the forecasting performance of the New Area-Wide Model, a small-open-economy DSGE model, to DSGEVARs, and to reduced-form linear Gaussian models.
Subjects: 
Bayesian inference
forecasting
Kalman filter
Missing data
Monte Carlo integration
JEL: 
C11
C32
C52
C53
E37
Document Type: 
Working Paper

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