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DSGE-based Priors for BVARs & Quasi-Bayesian DSGE Estimation

Thomai Filippeli (), Richard Harrison () and Konstantinos Theodoridis ()

No E2018/5, Cardiff Economics Working Papers from Cardiff University, Cardiff Business School, Economics Section

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.

Keywords: BVAR; SVAR; DSGE; DSGE-VAR; Gibbs Sampling; Marginal Likelihood Evaluation; Predictive Likelihood Evalution; Quasi-Bayesian DSGE Estimation (search for similar items in EconPapers)
JEL-codes: C11 C13 C32 C52 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge, nep-ecm and nep-ore
Date: 2018-01
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