Publication: Jointness in Bayesian Variable Selection with Applications to Growth Regression
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2006-11-01
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2006-11-01
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Abstract
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.
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“Ley, Eduardo; Steel, Mark F. J.. 2006. Jointness in Bayesian Variable Selection with Applications to Growth Regression. Policy Research Working Paper; No. 4063. © World Bank. http://hdl.handle.net/10986/8865 License: CC BY 3.0 IGO.”
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