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On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression

Eduardo Ley and Mark Steel ()

MPRA Paper from University Library of Munich, Germany

Abstract: 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 to 67 potential drivers of growth and 72 to 93 observations. Finally, we recommend priors for use in this and related contexts.

Keywords: Model size; Model uncertainty; Posterior odds; Prediction; Prior odds; Robustness (search for similar items in EconPapers)
JEL-codes: C11 O47 (search for similar items in EconPapers)
Date: 2008-01-06, Revised 2008-01-06
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Published in Journal of Applied Econometrics 24.4(2009): pp. 651-674

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Related works:
Working Paper: On the effect of prior assumptions in Bayesian model averaging with applications to growth regression (2007) Downloads
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