This class contains the GoGARCH
class and has the
outcome of optim
as an additional slot.
Objects can be created by calls of the form new("Goestnls", ...)
,
or with the function gogarch
whereby method = "nls"
has
been set.
nls
:Object of class "list"
: List returned by
optim
.
Z
:Object of class "matrix"
: Transformation matrix.
U
:Object of class "matrix"
: Orthogonal matrix.
Y
:Object of class "matrix"
: Extracted
component matrix.
H
:Object of class "list"
: List of conditional
variance/covariance matrices.
models
:Object of class "list"
: List of
univariate GARCH model fits.
estby
:Object of class "character"
: Estimation method.
X
:Object of class "matrix"
: The data matrix.
V
:Object of class "matrix"
: Covariance matrix
of X
.
P
:Object of class "matrix"
: Left singular
values of Var/Cov matrix of X
.
Dsqr
:Object of class "matrix"
: Square roots of
eigenvalues on diagonal, else zero.
garchf
:Object of class "formula"
: Garch
formula used for uncorrelated component GARCH models.
name
:Object of class "character"
: The name of
the original data object.
Returns the conditional variances as object with class attribute
"mts" "ts"
.
Returns the conditional co-variances as object with
class attribute "mts" "ts"
.
Returns the conditional correlationsas object with class
attribute "mts" "ts"
.
Returns the coeffiecients of the component GARCH models.
Returns the convergence codes of the component GARCH models.
Returns the formula for the component GARCH models.
NLS-Estimation of Go-GARCH models.
Plotting of the conditional correlations.
Returns the conditional covariances and mean
forecasts and the forecasts of the component GARCH models, object
is of class Gopredict
.
Returns the residuals of the Go-GARCH model as
object with class attribute "mts" "ts"
.
Returns the residuals of the Go-GARCH model as
object with class attribute "mts" "ts"
.
show-method for objects of class Goestnls
.
summary-method for objects of class GoGARCH
,
object is of class Gosum
.
Updates an object of class GoGARCH
.
'>GoGARCH
, '>Goinit
,
'>Gosum
, '>Gopredict
,
goest-methods
, gogarch