Hi,

I am programming in matlab for a project that has to do with system

identification with neural networks and i am going to use model

predicted output. Does anyone knows if there is a command in matlab

that can do so. If not i would like to know about suggestions that i

can do so.

1. cross-predict time series model

2. How to predict conditional variance with model

Hi,

I'm trying to use the Model in SAS/ETS to predict the conditional volatility

using various forms of GARCH. I used the sample code from the SAS website and

added to it. Right now, it looks like this:

proc model data = returns;

parms earch0 .1 earch1 .2 egarch1 .75 theta .65 ;

/* mean model */

lnrt = intercept ;

/* variance model */

if (_obs_ = 1 ) then

h.lnrt = exp(earch0/(1. - egarch1)) ;

else h.lnrt = exp(earch0 + earch1*zlag(g) + egarch1*log(zlag(h.lnrt))) ;

g = theta*(-nresid.lnrt) + abs(-nresid.lnrt) - sqrt(2/constant('pi')) ;

/* fit the model */

fit lnrt / covbest=GLS fiml method = marquardt hessian = GLS out=eg_hat normal

outest=eg_param outs=eg_cvm;

run;

/*Solve for 15-period forecast*/

solve h.lnrt / data=returns estdata=eg_param sdata=eg_cvm nahead=15

theil out=eg_fc;

run;

However, SAS gives me the following error message:

ERROR: The number of variables to solve for, 1, does not equal the number of

equations, 0, that the solution is to satisfy.

Am I approaching this the wrong way? Which equation has to be satisfied to solve

for h.lnrt? Is there a better way to get SAS to forecast n-periods conditional

variance (h.lnrt) for GARCH?

Any suggestions would be much appreciated as I'm just learning SAS for my thesis.

Thanks

Allen

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