Description Slots Methods Note Author(s)

Class for the ACD rolling forecast.

`forecast`

:Object of class

`"vector"`

`model`

:Object of class

`"vector"`

- 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.

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.

Alexios Ghalanos

racd documentation built on May 31, 2017, 2:06 a.m.

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