Ciccone and Jaroci-ski (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.
