Bayesian Model Averaging in R
Bayesian model averaging has increasingly witnessed applications across an array of empirical contexts. However, the dearth of available statistical software which allows one to engage in a model averaging exercise is limited. It is common for consumers of these methods to develop their own code, which has obvious appeal. However, canned statistical software can ameliorate one's own analysis if they are not intimately familiar with the nuances of computer coding. Moreover, many researchers would prefer user ready software to mitigate the inevitable time costs that arise when hard coding an econometric estimator. To that end, this paper describes the relative merits and attractiveness of several competing packages in the statistical environment R to implement a Bayesian model averaging exercise.
|Date of creation:||2011|
|Date of revision:|
|Publication status:||Forthcoming: Under Review|
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- Theo Eicher & Chris Papageogiou & Adrian E Raftery, 2007.
"Default Priors and Predictive Performance in Bayesian Model Averaging, with Application to Growth Determinants,"
UWEC-2007-25-P, University of Washington, Department of Economics.
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- Martin Feldkircher & Stefan Zeugner, 2009. "Benchmark Priors Revisited:on Adaptive Shrinkage and the Supermodel Effect in Bayesian Model Averaging," IMF Working Papers 09/202, International Monetary Fund.
- Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
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