gmwm_master_cpp {simts} | R Documentation |
Master Wrapper for the GMWM Estimator
Description
This function generates WV, GMWM Estimator, and an initial test estimate.
Usage
gmwm_master_cpp(
data,
theta,
desc,
objdesc,
model_type,
starting,
alpha,
compute_v,
K,
H,
G,
robust,
eff
)
Arguments
data |
A vec containing the data.
|
theta |
A vec with dimensions N x 1 that contains user-supplied initial values for parameters
|
desc |
A vector<string> indicating the models that should be considered.
|
objdesc |
A field<vec> containing a list of parameters (e.g. AR(1) = c(1,1), ARMA(p,q) = c(p,q,1))
|
model_type |
A string that represents the model transformation
|
starting |
A bool that indicates whether the supplied values are guessed (T) or are user-based (F).
|
alpha |
A double that handles the alpha level of the confidence interval (1-alpha)*100
|
compute_v |
A string that describes what kind of covariance matrix should be computed.
|
K |
An int that controls how many times theta is updated.
|
H |
An int that controls how many bootstrap replications are done.
|
G |
An int that controls how many guesses at different parameters are made.
|
robust |
A bool that indicates whether the estimation should be robust or not.
|
eff |
A double that specifies the amount of efficiency required by the robust estimator.
|
Value
A field<mat>
that contains a list of ever-changing estimates...
Author(s)
JJB
References
Wavelet variance based estimation for composite stochastic processes, S. Guerrier and Robust Inference for Time Series Models: a Wavelet-Based Framework, S. Guerrier
[Package
simts version 0.2.2
Index]