Implements the Dynamic Model Averaging procedure with the possibility of also performing averaging over a grid of foregetting factor values
1 2 3 
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 the grid of values of δ. By default 
dAlpha 
numeric variable representing α. By default 
vKeep 

bZellnerPrior 
Boolean variable indicating whether the Zellner prior should be used on 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 
dBeta 
integer indicating the forgetting factor for the measurement variance, see Prado and West (2010) pp. 132 and Beckmann and Schussler (2014). 
There might be situations when the practitioner desires to predict T+1 conditional on observation till time T in a true outofsample fashion. In such circumstances the user can substitute the future value of the dependent variable with an NA
. This way, the code treats the last observation as missing and does not perform backtesting or updating of the coefficients. However, the filter provides us with the necessary quantities to perform prediction. The predicted value \hat{y_{T+1}} = E[y_T+1  F_T] as well as the predicted variance decomposition can then be extracted using the getLastForecast method. The other quantities that can be extracted, for example via the as.data.frame method, will ignore the presence of the last NA
and report results only for the fist T observations.
See Catania and Nonejad (2016) for further details.
An object of the class DMA
, see DMAclass.
Leopoldo Catania & Nima Nonejad
Beckmann, J., & Schussler, R. (2014). Forecasting Equity Premia using Bayesian Dynamic Model Averaging (No. 2914). Center for Quantitative Economics (CQE), University of Muenster.
Dangl, T., & Halling, M. (2012). Predictive regressions with time–varying coefficients. Journal of Financial Economics, 106(1), 157–181. 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. doi: 10.1016/j.jfineco.2012.06.005. URL http://www.sciencedirect.com/science/article/pii/S0304405X12001316.
Prado, R., & West, M. (2010). Time series: modeling, computation, and inference. CRC Press.
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)

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