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)
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