27 Pages Posted: 10 Oct 2014
Date Written: June 27, 2014
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.
Keywords: Bayesian inference, density forecasting, Kalman filter, missing data, Monte Carlo integration, predictive likelihood
JEL Classification: C11, C32, C52, C53, E37
Suggested Citation: Suggested Citation
Warne, Anders and Coenen, Günter and Christoffel, Kai Philipp, Marginalized Predictive Likelihood Comparisons of Linear Gaussian State-Space Models with Applications to DSGE, DSGE-VAR, and VAR Models (June 27, 2014). CFS Working Paper, No. 478. Available at SSRN: https://ssrn.com/abstract=2507827 or http://dx.doi.org/10.2139/ssrn.2507827