TY - BOOK ID - 133407855 TI - On the Effect of Prior Assumptions in Bayesian Model Averaging With Applications To Growth Regression AU - Steel, Mark F. J. AU - Ley, Eduardo PY - 2007 PB - Washington, D.C., The World Bank, DB - UniCat KW - Area KW - Arts and Music KW - Binomial distribution KW - Classification KW - Culture & Development KW - Drawing KW - Drawings KW - Educational Technology and Distance Learning KW - Essays KW - Geographical Information Systems KW - Information Security and Privacy KW - Literature KW - Performances KW - Picture KW - Science and Technology Development KW - Simulation KW - Statistical and Mathematical Sciences UR - https://www.unicat.be/uniCat?func=search&query=sysid:133407855 AB - This paper examines 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. The paper analyzes the effect of a variety of prior assumptions on the inference concerning model size, posterior inclusion probabilities of regressors, and predictive performance. The analysis illustrates these issues in the context of cross-country growth regressions using three datasets with 41 to 67 potential drivers of growth and 72 to 93 observations. The results favor particular prior structures for use in this and related contexts. ER -