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QPBoot

Model validation using Quantile Spectral Analysis and Parametric Bootstrap techniques

This pakage can be used for validating parametric time-series models. There is a demo available for checking if a GARCH(1,1) model is suitable for DAX returns via

demo("DAX")

The main method is qpBoot and for its model argument there are several predefined models (getGARCH(), or getARMA() for example).

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Version

Install

install.packages('QPBoot')

Monthly Downloads

4

Version

0.2

License

GPL (>= 2)

Maintainer

Stefan Birr

Last Published

June 1st, 2017

Functions in QPBoot (0.2)

Estimate-tsModel

Models

Predefined Time-Series Models
QPBoot-class

Class for a Parametric Bootstrap based on Quantile Spectral Analysis
QPBoot-constructor

qpBoot
tsModel-class

Class for a Parametric Time-Series Model
compare.arg.names

Compare arguments with character vector
computeCIs-QPBoot

Pointwise Confidence Intervalls
QPBoot-package

Quantile Spectral Analysis for Parametric Bootstrap
Simulate-tsModel

dax

DAX: Deutscher Aktien Index 2000--2010
generics-accessors

Generic functions for accessing attributes of objects These generic functions are needed to access or set the objects' attributes.
setParameter-tsModel

setSimulate-tsModel

alphaq

Returns a function to retrieve the \(\alpha\)-quantile from a vector.
arg.names

Returns the argument names of a function.
plot-QPBoot

setEstimate-tsModel