On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression
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...
| Main Authors: | , |
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| Format: | Policy Research Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2012
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/2007/06/7712856/effect-prior-assumptions-bayesian-model-averaging-applications-growth-regression http://hdl.handle.net/10986/7401 |
| Summary: | 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. |
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