Affiliations: [a] Department of Economics, Virginia Polytechnic
Institute and State University, Blacksburg, VA, USA | [b] Department of Economics, University of Miami, Miami,
FL, USA
Correspondence:
[*]
Corresponding author: Christopher F. Parmeter,
Department of Economics, University of Miami, FL, USA. Tel.: +1 305 284 4397;
E-mail: cparmeter@bus.miami.edu
Abstract: 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.
