Description Usage Arguments Details Value Author(s) See Also Examples
This GUI allows realtime simulations of ARMA and GARCH processes. Further models can be added by the user.
1 
user 
a further class of model (or a list of such models), additional to ARCH(3,3) and GARCH(3,3) 
wait 
nonnegative integer. Users will usually not change this value.
if the value is greater than 0, the control over the command line of R
is returned to the user only after the tcltk window has been left.

included.models 

Sliders and Buttons of the GUI are:
The random seed is fixed when model parameters are changed. ‘New Innovations’ draws a new random seed
leaves the program
choose one of the given models to be displayed
length of the time series
the length of the burn in period
number of simulations (with different seeds) shown in the graphics
the distribution families of the independent errors. If more than one family is chosen the graphs are put on top of each other.
allows to freeze the current choice of the parameter values.
If it is pressed a new column appears with the frozen values. The
effect becomes apparent when now the sliders are moved.
The new column has a d
for deleting that column and a
1
to switch this column with the current columns of the
sliders.
The ARMA model is given by
Z_t  φ_1 Z_{t1} φ_2 Z_{t2} φ_3 Z_{t3} = \varepsilon_t + θ_1 \varepsilon_{t1} + θ2 \varepsilon_{t2} + θ3 \varepsilon_{t3}
The GARCH model is given by Z_t = σ_t \varepsilon_t and
σ_t^2  β_1 σ^2_{t1}  β_2 σ^2_{t2}  β_3 σ^2_{t3} = α_0 + α_1 Z_{t1}^2 + α_2 Z_{t2}^2
the parameters corresponding to the distributions of the INNOVATIONS
starting values for the burn in period
The argument user
allows to define
ARCH and GARCH models with different numbers of parameters,
see the examples in ARMA.model
and GARCH.model
.
The argument user
also allows to define one's own model.
The example of such a definition is returned by ARMA.model
.
While ARMA.model()
returns an overview and
str(ARMA.model(), give.attr=TRUE)
gives some more details,
the code print(ARMA.model(), details=TRUE)
gives the full
definition of a time series:
time
, burn.in
, repetitions
the default values of the respective sliders, see above
*.min
, *.max
The lower and upper bound of the slider values.
phi
named vector; the names will be the titles of the sliders; phi contains all model parameters
distr
note that all the three distribution functions have access
to the values of all three INNOVATION PARAMETERS in the gui, since
param
is a vector of three elements
distr.param
named vector of parameters. It could be also a shorter or longer vector than the standard vector of length 3
distr.show
at least one of the components must be TRUE
,
indicating the default distribution
titles
a list of 2 functions
titles[[1]]
returns the title of the model in dependence of the current values of the model parameters
titles[[2]]
returns a string of additional information on the model
update.function
for given innovations
, i.e. realisations of the errors
\varepsilon_i including burn in,
the parameter values φ of the model and
for given starting values start.ts
the function calculates
the time series, including burn in. Note that start.ts
is a matrix of length{param}
rows and n r
columns, where n is the number of chosen
distribution families and r is the number of repetitions.
Similar, innovations
is also
starting.ts
the starting values of the process in the burnin period of the simulation
starting.matrix.ts
This function turns the starting values starting.ts
into a matrix where is essentially the
starting values starting.ts
are written in each column.
The number of columns are the number of chosen distribution family
time the number of repetitions. Note that in the matrix the
repetitions are grouped to gether, i.e. if r is the number
of repetitions the first r rows refer to the r
repetitions of the first chosen distribution.
The function is introduced to allow also for transformations of
starting.ts
for different distributions.
starting.innovations
the starting values of the innovations in the burnin period of the simulation
starting.matrix.innovations
function analogue to starting.matrix.ts
A list with the latest chosen paremeters is returned.
Martin Schlather, schlather@math.unimannheim.de
1 2 
if (interactive()) tsgui()

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