Perform Dynamic Model Averaging
Description
Implements the Dynamic Model Averaging procedure with the possibility of different valued of the instability parameter.
Usage
1 2 
Arguments
formula 
an object of class formula (or one that can be coerced to that class): a symbolic description of the model to be fitted 
data 
an object of the class data.frame, (or object coercible by as.data.frame to a data frame) containing the variables in the model. It can also be an object of the classes 
vDelta 
D x 1 numeric vector representing the δ parameter. By default 
dAlpha 
numeric variable representing α. By default 
vKeep 

bZellnerPrior 
Boolean variable indicating whether the Zellner prior should be used for the coefficients at time t=0. Default = 
dG 
numeric variable equal to 100 by default. If 
bParallelize 
Boolean variable indicating whether to use multiple processors to speed up the computations. By default 
iCores 
integer indicating the number of cores to use if 
Details
See Catania and Nonejad (2016) for further details.
Value
An object of the class DMA
, see DMAclass.
Author(s)
Leopoldo Catania & Nima Nonejad
References
Dangl, T., & Halling, M. (2012). Predictive regressions with time–varying coefficients. Journal of Financial Economics, 106(1), 157–181. \Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("doi:10.1016/j.jfineco.2012.04.003")}.
Catania, Leopoldo, and Nima Nonejad. "Dynamic Model Averaging for Practitioners in Economics and Finance: The eDMA Package." arXiv preprint arXiv:1606.05656 (2016).
Paye, B.S. (2012). 'Deja vol': Predictive Regressions for Aggregate Stock Market Volatility Using Macroeconomic Variables.Journal of Financial Economics, 106(3), 527546. ISSN 0304405X. \Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("doi:10.1016/j.jfineco.2012.06.005")}. URL http://www.sciencedirect.com/science/article/pii/S0304405X12001316.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  ## Not run:
# Code chunk of Catania and Nonejad (2016) Fast Dynamic Model Averaging
# for Practitioners in Economics and Finance: The eDMA Package
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))
Fit
## End(Not run)
