Model uncertainty in cross-country growth regressions

Carmen Fernandez, Eduardo Ley and Mark Steel ()
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Carmen Fernandez: School of Mathematics and Statistics, University of Saint Andrews, UK, Postal: School of Mathematics and Statistics, University of Saint Andrews, UK

Journal of Applied Econometrics, 2001, vol. 16, issue 5, pages 563-576

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.

Date: 2001
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Working Paper: Model uncertainty in cross-country growth regressions (2001)
Working Paper: Model uncertainty in cross-country growth regressions (2001)
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