Ley, EduardoSteel, Mark F. J.2012-03-302012-03-302009Journal of Applied Econometrics08837252https://hdl.handle.net/10986/4690We 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 cross-country growth regressions using three datasets with 41-67 potential drivers of growth and 72-93 observations. Finally, we recommend priors for use in this and related contexts.ENSingle Equation ModelsSingle Variables: General C200Model Construction and Estimation C510Forecasting MethodsSimulation Methods C530Measurement of Economic GrowthAggregate ProductivityCross-Country Output Convergence O470Journal ArticleWorld Bank