On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression
AbstractWe 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 to 67 potential drivers of growth and 72 to 93 observations. Finally, we recommend priors for use in this and related contexts.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 6773.
Date of creation: 06 Jan 2008
Date of revision: 06 Jan 2008
Publication status: Published in Journal of Applied Econometrics 24.4(2009): pp. 651-674
Model size; Model uncertainty; Posterior odds; Prediction; Prior odds; Robustness;
Other versions of this item:
- Ley, Eduardo & Steel, Mark F. J., 2007. "On the effect of prior assumptions in Bayesian model averaging with applications to growth regression," Policy Research Working Paper Series 4238, The World Bank.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- O47 - Economic Development, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Measurement of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
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