Model uncertainty in cross-country growth regressions
We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian Model Averaging (BMA). We find that the posterior probability is very spread among many models suggesting the superiority of BMA over choosing any single model. Out-of-sample predictive results support this claim. In contrast with 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.
|Date of creation:||27 Mar 1999|
|Date of revision:||06 Oct 2001|
|Note:||Type of Document - Tex; prepared on MacOS, TeXtures; to print on any printer; figures: included. Forthcoming in the Journal of Applied Econometrics. Replaces the paper entitled: "We have just averaged over two trillion cross-country growth regressions" with ewp-em/0110002.|
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