On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression
25 Pages Posted: 20 Apr 2016
There are 2 versions of this paper
On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression
On the Effect of Prior Assumptions in Bayesian Model Averaging With Applications to Growth Regression
Date Written: June 1, 2007
Abstract
This paper examines the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. The paper analyzes the effect of a variety of prior assumptions on the inference concerning model size, posterior inclusion probabilities of regressors, and predictive performance. The analysis illustrates these issues in the context of cross-country growth regressions using three datasets with 41 to 67 potential drivers of growth and 72 to 93 observations. The results favor particular prior structures for use in this and related contexts.
Keywords: Educational Technology and Distance Education, Geographical Information Systems, Statistical & Mathematical Sciences, Science Education, Scientific Research & Science Parks
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
By Eduardo Ley and Mark F.j. Steel
-
Are Any Growth Theories Robust?
By Steven N. Durlauf, Andros Kourtellos, ...
-
Jointness of Growth Determinants
By Gernot Doppelhofer and Melvyn Weeks
-
Determinants of Economic Growth: Will Data Tell?
By Antonio Ciccone and Marek Jarocinski
-
Determinants of Economic Growth: Will Data Tell?
By Antonio Ciccone and Marek Jarocinski
-
Growth Empirics Under Model Uncertainty: Is Africa Different?
-
By Theo S. Eicher, Chris Papageorgiou, ...
-
Jointness in Bayesian Variable Selection with Applications to Growth Regression
By Eduardo Ley and Mark F.j. Steel
-
Jointness in Bayesian Variable Selection With Applications to Growth Regression
By Eduardo Ley and Mark F.j. Steel