Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/97342
Authors: 
Filippeli, Thomai
Theodoridis, Konstantinos
Year of Publication: 
2014
Series/Report no.: 
Working Paper, School of Economics and Finance, Queen Mary, University of London 713
Abstract: 
Similar to Ingram and Whiteman (1994), De Jong et al. (1993) and Del Negro and Schorfheide (2004) this study proposes a methodology of constructing Dynamic Stochastic General Equilibrium (DSGE) consistent prior distributions for Bayesian Vector Autoregressive (BVAR) models. The moments of the assumed Normal-Inverse Wishart (no conjugate) prior distribution of the VAR parameter vector are derived using the results developed by Fernandez-Villaverde et al. (2007), Christiano et al. (2006) and Ravenna (2007) regarding structural VAR (SVAR) models and the normal prior density of the DSGE parameter vector. In line with the results from previous studies, BVAR models with theoretical priors seem to achieve forecasting performance that is comparable - if not better - to the one obtained using theory free Minnesota priors (Doan et al., 1984). Additionally, the marginal-likelihood of the time-series model with theory founded priors - derived from the output of the Gibbs sampler - can be used to rank competing DSGE theories that aim to explain the same observed data (Geweke, 2005). Finally, motivated by the work of Christiano et al. (2010b,a) and Del Negro and Schorfheide (2004) we use the theoretical results developed by Chernozhukov and Hong (2003) and Theodoridis (2011) to derive the quasi Bayesian posterior distribution of the DSGE parameter vector.
Subjects: 
BVAR
SVAR
DSGE
Gibbs sampling
Marginal-likelihood evaluation
Predictive Density Evaluation
Quasi-Bayesian DSGE Estimation
JEL: 
C11
C13
C32
C52
Document Type: 
Working Paper

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