Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230411
Authors: 
Filippeli, Thomai
Harrison, Richard
Theodoridis, Konstantinos
Year of Publication: 
2018
Series/Report no.: 
Cardiff Economics Working Papers No. E2018/5
Abstract: 
We present a new method for estimating Bayesian vector autoregression (VAR) models using priors from a dynamic stochastic general equilibrium (DSGE) model. We use the DSGE model priors to determine the moments of an independent Normal-Wishart prior for the VAR parameters. Two hyper-parameters control the tightness of the DSGE-implied priors on the autoregressive coefficients and the residual covariance matrix respectively. Determining these hyper-parameters by selecting the values that maximize the marginal likelihood of the Bayesian VAR provides a method for isolating subsets of DSGE parameter priors that are at odds with the data. We illustrate the ability of our approach to correctly detect incorrect DSGE priors for the variance of structural shocks using a Monte Carlo experiment. We also demonstrate how posterior estimates of the DSGE parameter vector can be recovered from the BVAR posterior estimates: a new 'quasi-Bayesian' DSGE estimation. An empirical application on US data reveals economically meaningful differences in posterior parameter estimates when comparing our quasi-Bayesian estimator with Bayesian maximum likelihood. Our method also indicates that the DSGE prior implications for the residual covariance matrix are at odds with the data.
Subjects: 
BVAR
SVAR
DSGE
DSGE-VAR
Gibbs Sampling
Marginal Likelihood Evaluation
Predictive Likelihood Evalution
Quasi-Bayesian DSGE Estimation
JEL: 
C11
C13
C32
C52
Document Type: 
Working Paper

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