Marginalized predictive likelihood comparisons of linear Gaussian state-space models with applications to DSGE, DSGEVAR, and VAR models

Anders Warne, Günter Coenen and Kai Christoffel

No 478, CFS Working Paper Series from Center for Financial Studies (CFS)

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.

Keywords: Bayesian inference; density forecasting; Kalman filter; missing data; Monte Carlo integration; predictive likelihood (search for similar items in EconPapers)
JEL-codes: C11 C32 C52 C53 E37 (search for similar items in EconPapers)
Date: 2014
New Economics Papers: this item is included in nep-dge, nep-ecm, nep-ets, nep-for and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Marginalized Predictive Likelihood Comparisons of Linear Gaussian State‐Space Models with Applications to DSGE, DSGE‐VAR, and VAR Models (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfswop:478

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