# class: ACD Rolling Forecast Class

### Description

Class for the ACD rolling forecast.

### Slots

`forecast`

:Object of class

`"vector"`

`model`

:Object of class

`"vector"`

### Methods

- as.data.frame
`signature(x = "ACDroll")`

: Extracts various values from object (see note).- resume
`signature(object = "ACDroll")`

: Resumes a rolling backtest which has non-converged windows using alternative solver and control parameters.- coef
`signature(object = "ACDroll")`

: Extracts the list of coefficients for each estimated window in the rolling backtest.- show
`signature(object = "ACDroll")`

: Summary.- quantile
`signature(x = "ACDroll")`

: Calculates and returns, given a vector of probabilities (additional argument “probs”), the conditional quantiles of the rolling object as an xts matrix.- pit
`signature(object = "ACDroll")`

: Calculates and returns the conditional probability integral transform given the realized data and forecast density.- skew
`signature(object = "ACDroll")`

: conditional skew.- sigma
`signature(object = "ACDroll")`

: conditional volatility.- shape
`signature(object = "ACDroll")`

: conditional shape.- skewness
`signature(object = "ACDroll")`

: conditional skewness.- kurtosis
`signature(object = "ACDroll")`

: conditional excess kurtosis.- convergence
`signature(object = "ACDroll")`

: Returns the convergence code for the estimation windows, with 0 indicating that all have converged and 1 that there were non-converged windows. In the latter case the ‘nonconverged’ attribute is also printed of those windows which failed to converge.- show
`signature(object = "ACDroll")`

: Summary.

### Note

The `as.data.frame`

extractor method allows the extraction of either the
conditional forecast density or the VaR. It takes additional argument
`which`

with valid values either “density” or “VaR”.

The `coef`

method will return a list of the coefficients and their robust
standard errors (assuming the keep.coef argument was set to TRUE in the
acdroll function), and the ending date of each estimation window.

### Author(s)

Alexios Ghalanos