  Raftery, Karny, and Ettler (2010) introduce an estimation technique, which
they refer to as Dynamic Model Averaging (DMA). In their application, DMA is
used to predict the output strip thickness for a cold rolling mill, where the
output is measured with a time delay. Recently, DMA has also shown to be useful
in macroeconomic and financial applications. In this paper, we present the eDMA
package for DMA estimation implemented in R. The eDMA package is especially
suited for practitioners in economics and finance, where typically a large
number of predictors are available. Our implementation is up to 133 times
faster then a standard implementation using a single-core CPU. Thus, with the
help of this package, practitioners are able to perform DMA on a standard PC
without resorting to large clusters, which are not easily available to all
researchers. We demonstrate the usefulness of this package through simulation
experiments and an empirical application using quarterly U.S. inflation data.
