Model uncertainty in cross-country growth regressions
AbstractWe 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.
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Bibliographic InfoPaper provided by EconWPA in its series Econometrics with number 9903003.
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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Bayesian Model Averaging; Choice of Regressors; Economic Growth; Markov chain Monte Carlo; Prediction;
Other versions of this item:
- Carmen Fernandez & Eduardo Ley & Mark F. J. Steel, 2001. "Model uncertainty in cross-country growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 563-576.
- Carmen Fernandez & Eduardo Ley & Mark Steel, 2001. "Model uncertainty in cross-country growth regressions," Econometrics 0110002, EconWPA.
- O49 - Economic Development, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Other
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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