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On the effect of prior assumptions in Bayesian model averaging with applications to growth regression
Author(s) -
Ley Eduardo,
Steel Mark F.J.
Publication year - 2009
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.1057
Subject(s) - econometrics , prior probability , bayesian probability , context (archaeology) , bayesian inference , inference , model selection , regression , linear regression , regression analysis , computer science , statistics , mathematics , artificial intelligence , paleontology , biology
We consider 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. We examine the effect of a variety of prior assumptions on the inference concerning model size, posterior inclusion probabilities of regressors and on predictive performance. We illustrate these issues in the context of cross‐country growth regressions using three datasets with 41–67 potential drivers of growth and 72–93 observations. Finally, we recommend priors for use in this and related contexts. Copyright © 2009 John Wiley & Sons, Ltd.