Bayesian Prior Elicitation in DSGE Models: Macro- vs Micro-Priors
49 Pages Posted: 20 Jun 2013
Date Written: September 21, 2011
Bayesian approaches to the estimation of DSGE models are becoming increasingly popular. Prior knowledge is normally formalized either directly on deep parameters' values (‘microprior’) or indirectly, on macroeconomic indicators, e.g. moments of observable variables (‘macroprior’). We introduce a non-parametric macroprior which is elicited from impulse response functions and assess its performance in shaping posterior estimates. We find that using a macroprior can lead to substantially different posterior estimates. We probe into the details of our result, showing that model misspecification is likely to be responsible of that. In addition, we assess to what extent the use of macropriors is impaired by the need of calibrating some hyperparameters.
Keywords: DSGE models, Bayesian estimation, Prior distribution, Impulse response function
JEL Classification: C11, C51, E30
Suggested Citation: Suggested Citation