On the effect of prior assumptions in Bayesian model averaging with applications to growth regression
研究线性回归中变量选择问题，分析不同先验假设对贝叶斯模型平均的推断和预测性能的影响，并基于跨国增长数据推荐合适的先验设定。
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 41–67 potential drivers of growth and 72–93 observations. Finally, we recommend priors for use in this and related contexts. Copyright © 2009 John Wiley & Sons, Ltd.