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Model uncertainty in cross-country growth regressions Author info | Abstract | Publisher info | Download info | Related research | Statistics Carmen Fernandez (School of Mathematics and Statistics, University of Saint Andrews, UK)
Eduardo Ley (IMF Institute, International Monetary Fund, Washington, DC, USA)
Mark F. J. Steel (Institute of Mathematics and Statistics, University of Kent at Canterbury, UK)
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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.
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics .
Volume (Year): 16 (2001)
Issue (Month): 5 ()
Pages: 563-576
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Handle: RePEc:jae:japmet:v:16:y:2001:i:5:p:563-576Contact details of provider: Web page: http://www.interscience.wiley.com/jpages/0883-7252/
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References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile , click on "citations" and make appropriate adjustments.: Kormendi, Roger C. & Meguire, Philip G., 1985.
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