Model uncertainty in cross-country growth regressions

We investigate the issue of model uncertainty in cross-country growth regre ssions using Bayesian Model Averaging (BMA). We find that the posterior pro bability is spread widely among many models, suggesting the superiority of BMA over choosing any single model. Out-of-sample predictive results suppor t this claim. In contrast to Levine and Renelt (1992), our results broadly support the more 'optimistic' conclusion of Salai-Martin (1997b), namely th at some variables are important regressors for explaining cross-country gro wth patterns. However, care should be taken in the methodology employed. Th e approach proposed here is firmly grounded in statistical theory and immed iately leads to posterior and predictive inference. Copyright (C) 2001 John Wiley & Sons, Ltd.