The impact of data revisions on the robustness of growth determinants—a note on ‘determinants of economic growth: Will data tell?’
回应Ciccone和Jarociński关于贝叶斯模型平均对数据扰动敏感的观点，指出其不稳定性源于特定设定，通过固定国家样本和灵活先验可改善稳健性。
SUMMARY Ciccone and Jarociński ( American Economic Journal: Macroeconomics 2010; 2 : 222–246) show that inference in Bayesian model averaging (BMA) can be highly sensitive to small data perturbations. In particular, they demonstrate that the importance attributed to potential growth determinants varies tremendously over different revisions of international income data. They conclude that ‘agnostic’ priors appear too sensitive for this strand of growth empirics. In response, we show that the found instability owes much to a specific BMA set‐up: first, comparing the same countries over data revisions improves robustness; second, much of the remaining variation can be reduced by applying an evenly ‘agnostic’ but flexible prior. Copyright © 2012 John Wiley & Sons, Ltd.