# DMA-class: class: Class for the DMA class In LeopoldoCatania/eDMA: Dynamic Model Averaging with Grid Search

## 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.