On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression
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 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.
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| Main Authors: | , |
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| Format: | Journal Article biblioteca |
| Language: | EN |
| Published: |
2009
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| Subjects: | Single Equation Models, Single Variables: General C200, Model Construction and Estimation C510, Forecasting Methods, Simulation Methods C530, Measurement of Economic Growth, Aggregate Productivity, Cross-Country Output Convergence O470, |
| Online Access: | http://hdl.handle.net/10986/4690 |
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