Jointness In Bayesian Variable Selection With Applications To Growth Regression

Jointness In Bayesian Variable Selection With Applications To Growth Regression / Ley, Eduardo

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...

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Détails bibliographiques
Auteurs principaux:Ley, Eduardo
Autres auteurs:Steel, Mark F. J.
Format: Online-Resource
Langue:English
Publié:Washington, D.C : The World Bank, 2006
Sujets:
Arts and Music
Calibration
Climate Change
Counting
Covariance
Culture & Development
Data
Econometrics
Economic
Educational Technology and Distance Learning
Environment
Evaluation
Finance and Financial Sector Development
Financial Literacy
Indicators
Information Security and Privacy
Less
Linear Regression
Logarithms
Macroeconomics and Economic Growth
Matrix
Poverty Reduction
Precision
Pro-Poor Growth
Probab
Probabilities
Science and Technology Development
Statistical and Mathematical Sciences
Accès en ligne:URL des Erstveröffentlichers
Description
Résumé: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
Description matérielle:1 Online-Ressource (17 Seiten)

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