Journal
JOURNAL OF FORECASTING
Volume 34, Issue 2, Pages 133-144Publisher
WILEY-BLACKWELL
DOI: 10.1002/for.2328
Keywords
model uncertainty; prediction; economic growth; Bayesian methods; correlated regressors
Categories
Funding
- Oesterreichische Nationalbank under the Jubilaumsfond grant [14663]
Ask authors/readers for more resources
We compare the predictive ability of Bayesian methods which deal simultaneously with model uncertainty and correlated regressors in the framework of cross-country growth regressions. In particular, we assess methods with spike and slab priors combined with different prior specifications for the slope parameters in the slab. Our results indicate that moving away from Gaussian g-priors towards Bayesian ridge, LASSO or elastic net specifications has clear advantages for prediction when dealing with datasets of (potentially highly) correlated regressors, a pervasive characteristic of the data used hitherto in the econometric literature. Copyright (C) 2015 John Wiley & Sons, Ltd.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available