garchMFit#
Purpose#
Estimates GARCH-in-mean model.
Format#
- out1 = garchMFit(y, p[, q, c0])#
- out1 = garchMFit(dataset, formula, p[, q, c0])
- Parameters:
y (Matrix) – dependent variables.
x (Matrix) – independent variables.
dataset (string) – name of data set or null string.
formula (string) – formula string of the model. E.g. “y ~ X1 + X2” ‘y’ is the name of dependent variable, ‘X1’ and ‘X2’ are names of independent variables; E.g. “y ~ .” , ‘.’ means including all variables except dependent variable ‘y’;
p (scalar) – order of the GARCH parameters.
q (scalar) – Optional input order of the ARCH parameters.
c0 (struct) –
Optional input garchControl structure.
c0.density
scalar, density of error term, 0 - Normal, 1 - Student’s t, 3 - skew generalized t.
c0.asymmetry
scalar, if nonzero assymetry terms are added.
c0.inmean
scalar, GARCH-in-mean, square root of conditional variance is included in the mean equation.
c0.stConstraintsType
scalar, type of enforcement of stationarity requirements, 1 - roots of characteristic polynomial constrained outside unit circle, 2 - arch, GARCH parameters constrained to sum to less than one and greater than zero, 3 - none.
c0.cvConstraintsType
scalar, type of enforcement of nonnegative conditional variances, 0 - direct constraints, 1 - Nelson & Cao constraints.
c0.covType
scalar, type of covariance matrix of parameters, 1 - ML, 2 - QML, 3 - none.
- Returns:
out1 (struct) –
garchEstimationstructure.out1.aic
scalar, Akiake criterion.
out1.bic
scalar, Bayesian information criterion.
out1.lrs
scalar, likelihood ratio statistic.
out1.numObs
scalar, number of observations.
out1.df
scalar, degrees of freedom.
out1.par
instance of PV structure containing parameter estimates.
out1.retcode
scalar, return code. out1.moment KxK matrix, moment m?atrix of parameter estimates.
- 1:
normal convergence.
- 2:
forced exit.
- 3:
function calculation failed.
- 4:
gradient calculation failed.
- 5:
Hessian calculation failed.
- 6:
line search failed.
- 7:
error with constraints.
- 8:
function complex.
out1.moment
KxK matrix, moment matrix of parameter estimates.
out1.climits
Kx2 matrix, confidence limits.
Library#
tsmt
Source#
tsgarch.src
See also
Functions garchFit(), garchGJRFit()