Mixtures of g-priors for bayesian model averaging with economic applications
Eduardo Ley and
Mark Steel ()
DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística
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.
Keywords: Consistency; Model; uncertainty; Posterior; odds; Prediction; Robustness (search for similar items in EconPapers)
JEL-codes: C11 O47 (search for similar items in EconPapers)
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Journal Article: Mixtures of g-priors for Bayesian model averaging with economic applications (2012)
Working Paper: Mixtures of g-priors for Bayesian model averaging with economic applications (2011)
Working Paper: Mixtures of g-priors for Bayesian Model Averaging with economic application (2011)
Working Paper: Mixtures of g-priors for Bayesian model averaging with economic applications (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:ws112116
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