gprior-class: Class "gprior"

In BMS: Bayesian Model Averaging Library

Class "gprior"

Description

An object pertaining to a coefficient prior

Objects from the Class

A gprior object holds descriptions and subfunctions pertaining to coefficient priors. Functions such as bms or zlm rely on this class to 'convert' the output of OLS results into posterior expressions for a Bayesian Linear Model. Post-processing functions such as density.bma also resort to gprior objects.
There are currently three coefficient prior structures built into the BMS package, generated by the following functions (cf. Feldkircher and Zeugner, 2009) :
gprior.constg.init: creates a Zellner's g-prior object with constant g.
gprior.eblocal.init: creates an Empricial Bayes Zellner's g-prior.
gprior.hyperg.init: creates a hyper g-prior with a Beta-prior on the shrinkage parameter.
The following describes the necessary slots

Author(s)

Martin Feldkircher and Stefan Zeugner

References

Feldkircher, M. and S. Zeugner (2009): Benchmark Priors Revisited: On Adaptive Shrinkage and the Supermodel Effect in Bayesian Model Averaging, IMF Working Paper 09/202.

See Also

bms and zlm for creating bma or zlm objects.
Check the appendix of vignette(BMS) for a more detailed description of built-in priors.
Check http://bms.zeugner.eu/custompriors.php for examples.

Examples

data(datafls)
mm1=bms(datafls[,1:10], g="EBL")
gg=mm1$gprior.info # is the g-prior object, augmented with some posterior statistics

mm2=bms(datafls[,1:10], g=gg) #produces the same result

mm3=bms(datafls[,1:10], g=BMS:::.gprior.eblocal.init) 
#this passes BMS's internal Empirical Bayes g-prior object as the coefficient prior 
# - any other obejct might be used as well

BMS documentation built on Aug. 9, 2022, 5:08 p.m.