Dynamic Model Averaging with Grid Search

Perform dynamic model averaging with grid search as in Dangl and Halling (2012) using parallel computing.

Perform Dynamic Model Averaging with grid search as in Dangl and Halling (2012) using parallel computing.

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eDMA (Catania and Nonejad, 2016) implements the Dynamic Model Averaging (DMA) estimation technique of Raftery, Karny, and Ettler (2010) with the modifications of Dangl and Halling (2012) in R.

The latest stable version of eDMA is available at 'https://CRAN.R-project.org/package=eDMA'.

The latest development version of `GASeDMA is available at 'https://github.com/LeopoldoCatania/eDMA'.

Please cite eDMA in publications:

Catania, L. and Nonejad, N. (2017). eDMA: Dynamic Model Averaging with Grid Search. R package. https://CRAN.R-project.org/package=eDMA

Catania, L., and Nonejad, N. (2016). Dynamic Model Averaging for Practitioners in Economics and Finance: The eDMA Package. arXiv preprint arXiv:1606.05656


Reference manual

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1.5-3 by Leopoldo Catania, 3 years ago

Browse source code at https://github.com/cran/eDMA

Authors: Leopoldo Catania [aut,cre] , Nima Nonejad [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp

Depends on zoo, xts, methods, parallel

Linking to Rcpp, RcppArmadillo

System requirements: Requires the OpenMP library for parallel computing. If the OpenMP library is not available, the code is executed sequentially and a warning is printed.

See at CRAN