Model uncertainty in cross-country growth regressions
Eduardo Ley and
Mark Steel ()
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Carmen Fernandez: University of Saint Andrews
Econometrics from University Library of Munich, Germany
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.
Keywords: Bayesian Model Averaging; Choice of Regressors; Economic Growth; Markov chain Monte Carlo; Prediction (search for similar items in EconPapers)
JEL-codes: C11 C52 O49 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dev and nep-ecm
Note: Type of Document - Tex; prepared on MacOS, TeXtures; to print on any printer; figures: included. Forthcoming in the Journal of Applied Econometrics
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Journal Article: Model uncertainty in cross-country growth regressions (2001)
Working Paper: Model uncertainty in cross-country growth regressions (2001)
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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpem:0110002
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