simul.far.sde {far} | R Documentation |
FAR-SDE process simulation
Description
Simulation of a FAR process following an Stochastic Differential Equation
Usage
simul.far.sde(coef=c(0.4, 0.8), n=80, p=32, sigma=1)
Arguments
coef |
Numerical vertor. It contains the two values of the
coefficients ( |
n |
Integer. The number of observations generated. |
p |
Integer. The number of discretization points. |
sigma |
Numeric. The standard deviation (see details for more informations). |
Details
This function implements the simulation proposed by Besse and Cardot (1996) to simulate a FAR process following the Stochastic Differential Equation:
dX^{(2)}+a_2.dX+a_1.X=\code{sigma}.dW
Where dX^{(2)}
and dX
stand respectively for
the second and first derivate of the process X, and W is a brownian
process.
The coefficients a_1
and a_2
are the two first
elements of coef
.
The simulation use a order one approximation inspired by the work of Milstein, as described in Besse and Cardot (1996).
Value
A fdata
object containing one variable ("var") which is a
FAR(1) process of length n
with p
discretization
points.
Author(s)
J. Damon
References
Besse, P. and Cardot, H. (1996). Approximation spline de la prévision d'un processus fonctionnel autorégressif d'ordre 1. Revue Canadienne de Statistique/Canadian Journal of Statistics, 24, 467–487.
See Also
simul.far
, simul.far.wiener
,
simul.farx
, simul.wiener
.
Examples
far1 <- simul.far.sde()
summary(far1)
print(far(far1,kn=2))
par(mfrow=c(2,1))
plot(far1,date=1)
plot(select.fdata(far1,date=1:5),whole=TRUE,separator=TRUE)