Joint ness in Bayesian variable selection with applications to growth regression

By: Contributor(s): Material type: TextTextSeries: Policy Research Working Paper, no. 4063Publication details: Washington, D. C. The World Bank 2006Description: 16 pSubject(s): DDC classification:
  • 519.9 L3J6
Summary: The authors present a measure of jointness to explore dependence among regressors in the context of Bayesian model selection. The jointness measure they propose equals the posterior odds ratio between those models that include a set of variables and the models that only include proper subsets. They show its application in cross-country growth regressions using two data-sets from the model-averaging growth literature. http://documents.worldbank.org/curated/en/349931468178447562/Jointness-in-Bayesian-variable-selection-with-applications-to-growth-regression
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The authors present a measure of jointness to explore dependence among regressors in the context of Bayesian model selection. The jointness measure they propose equals the posterior odds ratio between those models that include a set of variables and the models that only include proper subsets. They show its application in cross-country growth regressions using two data-sets from the model-averaging growth literature.

http://documents.worldbank.org/curated/en/349931468178447562/Jointness-in-Bayesian-variable-selection-with-applications-to-growth-regression

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