Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5099060 | Journal of Economic Dynamics and Control | 2012 | 20 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Control and Optimization
Authors
Marco J. Lombardi, Giulio Nicoletti,