Description Usage Arguments Details Value Author(s) References Examples
Simulate Univariate GAS processes.
1 2 
fit 
An estimated object of the class uGASFit. By default 
T.sim 

kappa 

A 

B 

Dist 

ScalingType 

The function permits to simulate from an estimated uGASFit object. If fit
is not provided, the user
can specify a GAS model via the additional arguments kappa
, A
, B
, Dist
and ScalingType
.
All the information regarding the supported univariate conditional distributions can be investigated using the DistInfo function. The model is specified as
y_{t}\sim p(yθ_{t})
, where θ_{t} is the vector of parameters for the density p(y.). Note that, θ_{t} includes also those parameters that are not timevarying. The GAS recursion for θ_{t} is
θ_{t} = Λ(\tilde{θ}_{t})
,
\tilde{θ}_{t}=κ + A*s_{t1} + B*\tilde{θ}_{t1}
,
where Λ(.) is the mapping function (see UniMapParameters) and \tilde{θ}_{t} is the vector of
reparametrised parameters. The process is initialized at θ_{1}=(I  B)^{1}κ, where κ is
the vKappa
vector. The vector s_{t} is the scaled score of p(y.) with respect to \tilde{θ}_{t}.
See Ardia et. al. (2016a) for further details.
An object of the class uGASSim.
Leopoldo Catania
Ardia D, Boudt K and Catania L (2016a).
"Generalized Autoregressive Score Models in R: The GAS Package."
http://ssrn.com/abstract=2825380.
Creal D, Koopman SJ, Lucas A (2013).
"Generalized Autoregressive Score Models with Applications."
Journal of Applied Econometrics, 28(5), 777795.
doi: 10.1002/jae.1279.
Harvey AC (2013). Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series. Cambridge University Press.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32  # Simulate from a GAS process with Studentt conditional
# distribution, timevarying location, scale and fixed shape parameter.
library(GAS)
set.seed(786)
T.sim = 1000 # number of observations to simulate
Dist = "std" # conditional Student distribution
# vector of unconditional reparametrised parameters such that, the unconditional level of
# \eqn{\theta}_{t} is (0, 1.5 ,7), i.e. location = 0, scale = 1.5,
# degrees of freedom = 7.
kappa = c(0.0, log(1.5), log(72.01))
# in this way we specify that the shape parameter is constant while the score
# coefficients for the location and the scale
# parameters are 0.001 and 0.01, respectively.
A = matrix(c(0.001 , 0.0 , 0.0 ,
0.0 , 0.01 , 0.0 ,
0.0 , 0.0 , 0.0 ), 3, byrow = TRUE)
B = matrix(c(0.7 , 0.0 , 0.0 ,
0.0 , 0.98, 0.0 ,
0.0 , 0.0 , 0.0),3,byrow = TRUE) # Matrix of autoregressive parameters.
Sim = UniGASSim(fit = NULL, T.sim, kappa, A, B, Dist, ScalingType = "Identity")
Sim

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