Predictive likelihood comparisons with DSGE and DSGE-VAR models

Anders Warne, Günter Coenen and Kai Christoffel

No 1536, Working Paper Series from European Central Bank

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. JEL Classification: C11, C32, C52, C53, E37

Keywords: Bayesian inference; forecasting; Kalman filter; Missing data; Monte Carlo integration (search for similar items in EconPapers)
Date: 2013-04
New Economics Papers: this item is included in nep-dge, nep-ecm, nep-ets, nep-for and nep-ore
Note: 563011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

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