Publication: Mixtures of g-priors for bayesian model averaging with economic applications
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2011-07
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Abstract
We examine the issue of variable selection in linear regression
have a potentially large amount of possible covariates and economic theory offers
insufficient guidance on how to select the
Model Averaging presents
uncertainty. Our main interest here is the effect of the prior on the results, such as
posterior inclusion probabilities of regressors and predictive performance. We combine
a Binomial-Beta prior on model size with a g
addition, we assign a hyperprior to g, as the choice
impact on the results. For the prior
of Beta shrinkage
priors, which covers most choices in the recent literature. We propose a benchmark Beta
prior, inspired by earlier findings with fixed g, and show it leads to
selection. Inference is conducted through a Markov chain Monte Carlo sampler over
model space and g. We examine the performance of the various priors in the context of
simulated and real data. For the latter, we consider two important appl
economics, namely cross-country growth regression and returns to schooling.
Recommendations to applied users are provided.
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Consistency, Model uncertainty, Posterior odds, Prediction, Robustness