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eDMA

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

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

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Version

Install

install.packages('eDMA')

Monthly Downloads

209

Version

1.5-3

License

GPL (>= 2)

Maintainer

Leopoldo Catania

Last Published

August 27th, 2018

Functions in eDMA (1.5-3)

PowerSet

Build the power set of the values {0,1,2,...,iK}.
USRecessions

data: Dates of U.S. recessions as inferred by GDP-based recession indicator (JHDUSRGDPBR).
BacktestDMA

Backtest measures for Dynamic Model Averaging and comparison with Dynamic Model Selection
eDMA-package

Dynamic Model Averaging with Modifications
DMA-class

class: Class for the DMA class
DMA

Perform Dynamic Model Averaging
Lag

Lag a vector or a matrix of observations
SimData

data: Simulated data from DLM of West and Harrison (1999).
USData

data: Quarterly US inflation and associated predictors
SimulateDLM

Simulate from DLM of West and Harrison (1999).