fDMA: Dynamic Model Averaging and Dynamic Model Selection for Continuous Outcomes

Allows to estimate dynamic model averaging, dynamic model selection and median probability model. The original methods are implemented, as well as, selected further modifications of these methods. In particular the user might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using 'Google Trends'. The code is written in a way which minimises the computational burden (which is quite an obstacle for dynamic model averaging if many variables are used). For example, this package allows for parallel computations and Occam's window approach. The package is designed in a way that is hoped to be especially useful in economics and finance. Main reference: Raftery, A.E., Karny, M., Ettler, P. (2010) <doi:10.1198/TECH.2009.08104>.

Version: 2.2.1
Imports: doParallel, forecast, foreach, gplots, graphics, grDevices, iterators, MSwM, parallel, psych, png, Rcpp, stats, tseries, utils, xts, zoo
LinkingTo: Rcpp, RcppArmadillo
Published: 2017-11-24
Author: Krzysztof Drachal [aut, cre] (Faculty of Economic Sciences, University of Warsaw, Poland)
Maintainer: Krzysztof Drachal <kdrachal at wne.uw.edu.pl>
License: GPL-3
URL: https://CRAN.R-project.org/package=fDMA
NeedsCompilation: yes
Citation: fDMA citation info
Materials: NEWS
CRAN checks: fDMA results

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Reference manual: fDMA.pdf
Package source: fDMA_2.2.1.tar.gz
Windows binaries: r-devel: fDMA_2.2.1.zip, r-release: fDMA_2.2.1.zip, r-oldrel: fDMA_2.2.1.zip
OS X El Capitan binaries: r-release: fDMA_2.2.1.tgz
OS X Mavericks binaries: r-oldrel: fDMA_2.2.1.tgz
Old sources: fDMA archive

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