Description Objects from the Class Slots Methods Note Author(s) References
Class for the DMA estimate.
A virtual Class: No objects may be created from it.
model
:Object of class "list"
Contains information about the DMA specification.
data
:Object of class "list"
Contains the data given to the DMA
function.
Est
:Object of class "list"
Contains the estimated quantities.
signature(object = "DMA")
:
Extracts estimated quantities, (see note).
signature(x = "DMA", y = "missing")
:
Plots estimated quantities.
signature(object = "DMA")
.
signature(object = "DMA")
:
Print a summary of the estimated model. This method accepts the additional argument iBurnPeriod
corresponding at the length of the burn-in period. By default iBurnPeriod = NULL
, i.e. all the sample is used.
signature(object = "DMA")
:
Extract the filtered regressor coefficients. This method accepts the additional argument iBurnPeriod
corresponding at the length of the burn-in period. By default iBurnPeriod = NULL
, i.e. all the sample is used.
signature(object = "DMA")
:
Extract the residuals of the model. This method accepts the additional argument iBurnPeriod
corresponding at the length of the burn-in period. By default iBurnPeriod = NULL
, i.e. all the sample is used. The additional Boolean argument standardize
controls if standardize residuals should be returned. By default standardize = FALSE
. The additional argument type
permits to choose between residuals evaluated using DMA or DMS. By default type = "DMA"
.
signature(object = "DMA")
:
Extract the inclusion probabilities of the regressors. This method accepts the additional argument iBurnPeriod
corresponding at the length of the burn-in period. By default iBurnPeriod = NULL
, i.e. all the sample is used.
signature(object = "DMA")
:
Extract the predictive log-likelihood series. This method accepts the additional argument iBurnPeriod
corresponding at the length of the burn-in period. By default iBurnPeriod = NULL
, i.e. all the sample is used. The additional argument type
permits to choose between predictive likelihood evaluated using DMA or DMS. By default type = "DMA"
.
signature(object = "DMA")
:
If the last observation of the dependent variable was NA
, i.e. the practitioner desidered to predict Y_{T+1} having a sample of length T (without backtesting the result), this method can be used to extract the predicted value \hat{y_T+1} = E[y_{T+1} | F_T] as well as the predicted variance decomposition according to Equation (12) of Catania and Nonejad (2016).
The as.data.frame()
method permits to extract several estimated quantities. It accepts the two additional arguments: which
with possible values:
mincpmt
: Posterior inclusion probabilities of the predictors.
vsize
: Expected number of predictors (average size).
mtheta
: Filtered estimates of the regression coefficients.
mpmt
: Posterior probability of the degree of instability.
vyhat
: Point forecasts.
vLpdfhat
: Predictive log-likelihood.
vdeltahat
: Posterior weighted average of delta.
mvdec
: representing the y_t variance decomposition. The function returns a T x 5 matrix whose columns contains the variables.
vtotal
: total variance.
vobs
: Observational variance.
vcoeff
: Variance due to errors in the estimation of the coefficients.
vmod
: Variance due to model uncertainty.
vtvp
: Variance due to uncertainty with respect to the choice of the degrees of time–variation in the regression coefficients.
vhighmp_DMS
: Highest posterior model probability.
vhighmpTop01_DMS
: Sum of the 10% highest posterior model probabilities.
and iBurnPeriod
which is an integer indicating the length of the burn-in period. For instance, if iBurnPeriod = 50
the first 50 observations are removed from the output. By default iBurnPeriod = NULL
meaning that all the observations are returned.
Leopoldo Catania & Nima Nonejad
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.
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