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Eduardo Ley
;
Mark F. J. Steel

on the effect of prior assumptions in bayesian model averaging with applications to growth regression (replication data)

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.

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Ley, Eduardo; Steel, Mark F. J. (2009): On the effect of prior assumptions in Bayesian model averaging with applications to growth regression (replication data). Version: 1. Journal of Applied Econometrics. Dataset. http://dx.doi.org/10.15456/jae.2022319.1306678321

Related Publication

Ley, E. and Steel, M. (2009), On The Effect Of Prior Assumptions In Bayesian Model Averaging With Applications To Growth Regression, Journal of Applied Econometrics, 24(4), 651-674. https://doi.org/10.1002/jae.1057

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