eDMA-package: Dynamic Model Averaging with Modifications

Description Details Author(s) References See Also Examples

Description

Perform Dynamic Model Averaging with modifications introduced in Dangl and Halling (2012) using parallel computing.

Details

Package: eDMA
Type: Package
Version: 1.5-1
Date: 2018-04-25
License: GPL (>= 2)

Author(s)

Leopoldo Catania & Nima Nonejad

Maintainer: Leopoldo Catania <[email protected]>

References

Raftery, Adrian E., Miroslav Karny, and Pavel Ettler. "Online prediction under model uncertainty via dynamic model averaging: Application to a cold rolling mill." Technometrics 52.1 (2010): 52-66.

Catania, Leopoldo, and Nima Nonejad (2018). "Dynamic Model Averaging for Practitioners in Economics and Finance: The eDMA Package." Journal of Statistical Software, 84(11), 1-39. doi: 10.18637/jss.v084.i11.

Dangl, Thomas, and Michael Halling. "Predictive regressions with time-varying coefficients." Journal of Financial Economics 106.1 (2012): 157-181.

Raftery, Adrian E., David Madigan, and Jennifer A. Hoeting. "Bayesian model averaging for linear regression models." Journal of the American Statistical Association 92.437 (1997): 179-191.

Harrison, Jeff, and Mike West. Bayesian Forecasting & Dynamic Models. Springer, 1999.

See Also

DMA

Examples

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library(eDMA)

## load data
data("USData")

## do DMA, keep the first three predictors fixed and the intercept
Fit <- DMA(GDPDEF ~ Lag(GDPDEF, 1) + Lag(GDPDEF, 2) + Lag(GDPDEF, 3) +
            Lag(ROUTP, 1) + Lag(UNEMP, 1), data = USData, vDelta = c(0.9,0.95,0.99),
             vKeep = c(1, 2, 3, 4))

eDMA documentation built on April 25, 2018, 5:04 p.m.