Default priors and predictive performance in Bayesian model averaging, with application to growth determinants
比较了12种参数先验和2种模型先验在贝叶斯模型平均中的表现，发现单位信息先验加均匀模型先验在预测增长决定因素时效果最好。
Bayesian model averaging (BMA) has become widely accepted as a way of accounting for model uncertainty, notably in regression models for identifying the determinants of economic growth. To implement BMA the user must specify a prior distribution in two parts: a prior for the regression parameters and a prior over the model space. Here we address the issue of which default prior to use for BMA in linear regression. We compare 12 candidate parameter priors: the unit information prior (UIP) corresponding to the BIC or Schwarz approximation to the integrated likelihood, a proper data-dependent prior, and 10 priors considered by Fernández et al. (Journal of Econometrics 2001; 100: 381–427). We also compare two model priors: the uniform model prior and a prior with prior expected model size 7. We compare them on the basis of cross-validated predictive performance on a well-known growth dataset and on two simulated examples from the literature. We found that the UIP with uniform model prior generally outperformed the other priors considered. It also identified the largest set of growth determinants. Copyright © 2009 John Wiley & Sons, Ltd.