On the effect of prior assumptions in Bayesian model averaging with applications to growth regression
Ley, Eduardo and Steel, Mark F. J. (2007) On the effect of prior assumptions in Bayesian model averaging with applications to growth regression. Working Paper. University of Warwick. Centre for Research in Statistical Methodology, Coventry.
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We consider the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. We examine the effect of a variety of prior assumptions on the inference concerning model size, posterior inclusion probabilities of regressors and on predictive performance. We illustrate these issues in the context of crosscountry growth regressions using three datasets with 41 to 67 potential drivers of growth and 72 to 93 observations. Finally, we recommend priors for use in this and related contexts.
|Item Type:||Working or Discussion Paper (Working Paper)|
|Subjects:||Q Science > QA Mathematics|
|Divisions:||Faculty of Science > Statistics|
|Library of Congress Subject Headings (LCSH):||Regression analysis, Bayesian statistical decision theory|
|Series Name:||Working papers|
|Publisher:||University of Warwick. Centre for Research in Statistical Methodology|
|Place of Publication:||Coventry|
|Number of Pages:||24|
|Status:||Not Peer Reviewed|
|Access rights to Published version:||Open Access|
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