Arbeitspapier
DSGE-based priors for BVARs & quasi-Bayesian DSGE estimation
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Cardiff Economics Working Papers ; No. E2018/5
- Klassifikation
-
Wirtschaft
Bayesian Analysis: General
Estimation: General
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Model Evaluation, Validation, and Selection
- Thema
-
BVAR
SVAR
DSGE
DSGE-VAR
Gibbs Sampling
Marginal Likelihood Evaluation
Predictive Likelihood Evalution
Quasi-Bayesian DSGE Estimation
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Filippeli, Thomai
Harrison, Richard
Theodoridis, Konstantinos
- Ereignis
-
Veröffentlichung
- (wer)
-
Cardiff University, Cardiff Business School
- (wo)
-
Cardiff
- (wann)
-
2018
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Filippeli, Thomai
- Harrison, Richard
- Theodoridis, Konstantinos
- Cardiff University, Cardiff Business School
Entstanden
- 2018
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