eDMA (version 1.5-4)

DMA-class: class: Class for the DMA class

Description

Class for the DMA estimate.

Objects from the Class

A virtual Class: No objects may be created from it.

Slots

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.

Methods

as.data.frame

signature(object = "DMA"): Extracts estimated quantities, (see note).

plot

signature(x = "DMA", y = "missing"): Plots estimated quantities.

show

signature(object = "DMA")

.

summary

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.

coef

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.

residuals

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".

inclusion.prob

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.

pred.like

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".

getLastForecast

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).

Author

Leopoldo Catania & Nima Nonejad

References

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. tools:::Rd_expr_doi("10.18637/jss.v084.i11").