DMA-class: class: Class for the DMA class

In eDMA: Dynamic Model Averaging with Grid Search

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).

Note

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.

Author(s)

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v084.i11")}.

eDMA documentation built on June 8, 2025, 1:56 p.m.