Model uncertainty in cross‐country growth regressions
用贝叶斯模型平均方法研究跨国增长回归中的模型不确定性，发现后验概率分散在许多模型中，支持BMA优于单一模型，并验证了部分变量对解释跨国增长模式的重要性。
Abstract We investigate the issue of model uncertainty in cross‐country growth regressions using Bayesian Model Averaging (BMA). We find that the posterior probability is spread widely among many models, suggesting the superiority of BMA over choosing any single model. Out‐of‐sample predictive results support this claim. In contrast to Levine and Renelt ( 1992 ), our results broadly support the more ‘optimistic’ conclusion of Sala‐i‐Martin ( 1997b ), namely that some variables are important regressors for explaining cross‐country growth patterns. However, care should be taken in the methodology employed. The approach proposed here is firmly grounded in statistical theory and immediately leads to posterior and predictive inference. Copyright © 2001 John Wiley & Sons, Ltd.