Grocer Function
NAME
garch_grad2 - Generates garch gradient
CALLING SEQUENCE
[g,dht,gradt]=garch_grad2(parm,nar,nma,y,x)
PARAMETERS
Input
-
parm= vector of parameters (beta, a0, ar and ma in that order)
-
nar = # of ar parameters
-
nma = # of ma parameters
-
y = (n x 1) vector of the endogenous variable
-
x = (n x k) vector of the exogenous variables
Output
-
g = (k+nar+nma+1 x 1) -gradient at param
- dht = (nobs x nar+nma+1) derivative of sigt w.r.t a0, ar, ma
- scores = (k+nar+nma+1 x 1) sub-gradient at each date
DESCRIPTION
Generates garch likelihood gradient. The maximised parameters are the parameters before the transformation that insures the positivity of the variance, whereas garch_gard applies to the true garch parameters, that is the ones obtained after this transformation.
EXAMPLE
g=garch_grad2(p,nar,nma,grocer_y,grocer_x)
Example taken from function garch().
AUTHOR
Eric Dubois 2002