What it does
- Which set of the covariates is appropriate to explain variation of the response variable? Are my results robust to in- / exclusion of additional explanatory variables? In addressing these issues Bayesian model averaging
(BMA) has become a popular alternative to model selection. The BMS (Bayesian Model
Sampling) package implements
Bayesian model averaging
- The BMS package excels in offering a range
of widely used prior structures
coupled with efficient MCMC algorithms
to sort through the model space.
It allows for uniform and
binomial-beta priors on the model
space as well as
informative prior inclusion
probabilities. Via these customized
model priors one can thus fuse prior
beliefs into the otherwise purely
agnostic analysis, that is prevalent in the
applied literature using BMA.
package also provides various
specifications for Zellner's
g prior including the so-called hyper-g
priors advocated in Liang et
al. (2008); Ley and Steel (2010);
Feldkircher and Zeugner (2009).
The sensitivity of BMA results to the
specification of Zellner's g prior is
well documented in the literature
(Feldkircher and Zeugner 2011).
The package comes along with numerous graphical tools to
analyze posterior coefficient
densities, the posterior model size or
predictive densities. It also includes
a graphical representation of the
model space via an image plot.
The BMS homepage
tutorials on the usage
of BMS as well as
covered in the
section. These include
the use of model
BMA in the context of
implementation of the
and Weeks, 2009) and
- The package is fully described
covering a range of
hands-on examples in: Bayesian Model Averaging Employing Fixed and Flexible Priors - The BMS Package for R,
together with Stefan Zeugner.
In: Journal of
Statistical Software , Vol. 68(4),
pp. 1-37, 2015.
- Amini and Parmeter (2011) and Amini and
Parmeter (2012) carry out a
comparison of R software packages that
implement Bayesian model
averaging, in particular the packages BAS
(Merlise Clyde 2012) and BMA (Adrian
Raftery and Jennifer Hoeting and Chris
and Ian Painter and Ka Yee Yeung
2012). Amini and Parmeter (2012)
conclude that BMS is the only among
its competitors that is
able the reproduce empirical
results in Fernandez et al. (2001);
Doppelhofer and Weeks (2009) and the
working paper version of Masanjala and
Papageorgiou (2008). See also Blazejowski and
Kwiatkowski (2015) for a more recent comparison.
BMS Add-ons and Tutorials
spatBMS_0.0.tar.gz (works only with R<=2.11)
spatBMS.zip (works only with R<=2.11)
spatBMS_0.1.tar.gz (works only with R>2.11)
spatBMS_0.1.zip (works with R>2.11)
- Amini SM, Parmeter CF (2011).
Averaging in R.
Journal of Economic and
Social Measurement, 36:4, pp. 253-287
- Amini SM, Parmeter CF (2012).
Comparisons of Model Averaging Techniques: Assessing Growth Determinants.
Journal of Applied
Econometrics, Vol. 27:5, 1099-1255.
midková, and Vašiček (2013);
Leading Indicators of Crisis Incidence: Evidence from Developed Countries.
Journal of International
Money and Finance, Vol. 35, pp1-19.
- Blazejowski and Kwiatkowski (2015);
Bayesian Model Averaging and Jointness Measures for gretl.
Journal of Statistical Software, Vol. 68(5), pp1-19.
- Chipman H (1996).
Bayesian Variable Selection with Related Predictors.
Canadian Journal of
Statistics, 24, pp. 17-36
- Crespo Cuaresma J, Doppelhofer G, Feldkircher M (2014).
The determinants of economic growth in European regions.
Regional Studies, Vol. 48, Nr. 1, pp. 44-67.
- Crespo Cuaresma J, Feldkircher M (2013).
Spatial Filtering, Model Uncertainty and the Speed of Income Convergence in Europe.
Journal of Applied Econometrics, Vol. 28, Issue 4, pp. 720-741.
- Doppelhofer G, Weeks M (2009).
Jointness of Growth Determinants.
Journal of Applied Econometrics, 24(2), 209-244
- Feldkircher M (2012).
Forecast Combination and Bayesian Model Averaging - A Prior Sensitivity Analysis.
Journal of Forecasting, 31, 361-376.
- Fernández C, Ley E, Steel MF (2001).
Model Uncertainty in Cross-Country Growth Regressions.
Journal of Applied Econometrics, 16, 563-576
- Giannone D, Lenza M, Reichlin L (2011).
Market Freedom and the Global Recession.
IMF Economic Review, 59, 111-135
- Horváth R (2013).
Does Trust Promote Growth?
Journal of Comparative
Studies, Vol. 41:3, pp. 777-788.
- Horváth R (2011).
Research & development and growth: A Bayesian model averaging analysis.
Economic Modelling, 28:6, 2669-2673
- Horváth, Rusnák,
midková, and Zapal (2011).
Dissent voting behavior of central
bankers: what do we really know?.
Economic Modelling, 28:6, 2669-2673
- Masanjala WH, Papageorgiou C (2008).
Rough and Lonely Road to
Prosperity: A Reexamination
of the Sources of Growth in
Africa Using Bayesian Model
Applied Econometrics, 23,
- Adrian Raftery and Jennifer Hoeting and Chris Volinsky and Ian Painter and Ka Yee Yeung (2012).
recent R package version.
- Merlise Clyde (2012).
Adaptive Sampling for Bayesian
recent R package version .