Bayesian Model Averaging in R
AbstractBayesian 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.
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Bibliographic InfoPaper provided by University of Miami, Department of Economics in its series Working Papers with number 2011-9.
Length: 35 pages
Date of creation: 2011
Date of revision:
Publication status: Forthcoming: Under Review
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Model Averaging; Zellner's g Prior; BMS;
Find related papers by JEL classification:
- C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-09-16 (All new papers)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Gernot Doppelhofer & Melvyn Weeks, 2007.
"Jointness of Growth Determinants,"
CESifo Working Paper Series
1978, CESifo Group Munich.
- Winford H. Masanjala & Chris Papageorgiou, 2008. "Rough and lonely road to prosperity: a reexamination of the sources of growth in Africa using Bayesian model averaging," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 671-682.
- 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.
- Theo S. Eicher & Chris Papageorgiou & Adrian E. Raftery, 2011. "Default priors and predictive performance in Bayesian model averaging, with application to growth determinants," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(1), pages 30-55, January/F.
- 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.
- Paul Millar, 2005. "BIC: Stata module to evaluate the statistical significance of variables in a model," Statistical Software Components S449507, Boston College Department of Economics, revised 14 Apr 2011.
- Blazejowski, Marcin & Kwiatkowski, Jacek, 2013. "Bayesian Model Averaging and Jointness Measures for gretl," MPRA Paper 44322, University Library of Munich, Germany.
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