Get covariance matrix in middle of iteration in cmlmt module?

The covariance matrix is computed only at the end of iterations, so this is what I suggest:  set the MaxIters member of the cmlmtControl structure to some limited amount, five say, then print (or save) the covariance matrix, the covPar member of the cmlmtResults structure.  Also save the estimates, the Par member of the cmlmtResults structure and use as start values for another run.  This could be put in a loop and you could print every fifth covariance matrix of the parameters up to convergence.

library cmlmt;
 
y = // endogenous data
 
x = // exogenous data
 
struct PV p0;
 
po = pvPack(pvcreate,@starting point@,"b");
 
proc fct(p0,y,x,ind);
 
......   likelihood calculation
 
endp;
 
struct cmlmtControl c0;
 
c0.maxiters = 5;
 
convergence = 1;
 
do until convergence == 0;
 
    struct cmlmtResults  = out;
 
    out = cmlmt(&fct,p0,y,x,c0);
 
    print out.covpar;
 
    p0 = out.par;
 
    convergence = out.retcode;
 
endo;

Hi Mr Ron Schoenberg

Thank you for answering my question. Unfortunately again errors which already happened, still remain and I can not solve it. if it is possible, I send you code and data.  and guide me how I solve this dilemma

I write code here but for data please send me your Email address.

// Libraries
library cmlmt,pgraph;
 
// Data
 
x=xlsreadm("datafilename","a1:b100",1,0);
 
y=x-meanc(x)';
 
n=rows(y);
 
k=cols(y);
 
struct DS d0;
 
d0=dsCreate;
 
d0.DataMatrix=y;
 
// Declarations
 
varred=vcx(y);
 
epsilon=y';
 
avec=zeros(k,n);
 
lnvar=zeros(k,n);
 
lnvar[.,1]=ln(diag(varred));
 
var=diagrv(eye(k),exp(lnvar[.,1]));
 
ind=1|1|1;
 
// Parameters
 
struct PV p;
 
a12=0;
 
b12=0;
 
b122=0;
 
b123=0;
 
a21=0;
 
b21=0;
 
b212=0;
 
b213=0;
 
A=1~avec[1,1]|avec[2,1]~1;
 
g=eye(k)*0.95;
 
d=eye(k)*0.15;
 
f=eye(k)*(-0.09);
 
c=lnvar[.,1].*(1-diag(g));
 
uncs=A*varred*A';
 
rho=uncs[1,2]/sqrt(uncs[1,1]*uncs[2,2]);
 
p=pvPack(pvCreate,a12,"a12");
 
p=pvPack(p,b12,"b12");
 
p=pvPack(p,b122,"b122");
 
p=pvPack(p,b123,"b123");
 
p=pvPack(p,a21,"a21");
 
p=pvPack(p,b21,"b21");
 
p=pvPack(p,b212,"b212");
 
p=pvPack(p,b213,"b213");
 
p=pvPack(p,c,"c");
 
p=pvPack(p,g,"g");
 
p=pvPack(p,d,"d");
 
p=pvPack(p,f,"f");
 
p=pvPack(p,rho,"rho");
 
// Algorithm settings
 
struct cmlmtControl c0;
 
c0=cmlmtControlCreate;
 
c0.Algorithm=4;
 
c0.LineSearch=4;
 
c0.CovParType=2;
 
c0.printIters=1;
 
c0.DirTol=0.0001;
 
//c0.TrustRadius=0.001;
 
//c0.Switch=1|10000000|0;
 
//c0.Switch={3 1, 0.1 0.1, 1 1, .001 .001};
 
// Start likelihood procedure
 
proc likproc(struct PV p, struct DS d0, ind);
 
    // Declarations
 
    local i,llik,a12,b12,a21,b21,c,g,d,f,A,trans,b122,b123,b212,b213,rho;
 
    llik=zeros(n,1);
 
    A=1~avec[1,1]|avec[2,1]~1;
 
    a12=pvUnpack(p,"a12");
 
    b12=pvUnpack(p,"b12");
 
    b122=pvUnpack(p,"b122");
 
    b123=pvUnpack(p,"b123");
 
    a21=pvUnpack(p,"a21");
 
    b21=pvUnpack(p,"b21");
 
    b212=pvUnpack(p,"b212");
 
    b213=pvUnpack(p,"b213");
 
    c=pvUnpack(p,"c");
 
    g=pvUnpack(p,"g");
 
    d=pvUnpack(p,"d");
 
    f=pvUnpack(p,"f");
 
    rho=pvUnpack(p,"rho");
 
    y=d0.DataMatrix;
 
    // Loop of likelihood for each observation
 
    i=2;
 
    do while i<=n;
 
        // EGARCH for structural conditional variance process
 
        lnvar[.,i]=c+g*lnvar[.,i-1]+d*(abs(epsilon[.,i-1]./sqrt(exp(lnvar[.,i-1])))-sqrt(2/pi))+f*(epsilon[.,i-1]./sqrt(exp(lnvar[.,i-1])));
 
        // Transition variable = demeaned sqrt-variance
 
        trans=sqrt(exp(lnvar[.,i]));
        //-sqrt(exp(c./(1-diag(g))));
 
        // Linear Variance Spillover vs. Taylor vs. Smooth Transition
 
        avec[.,i]=a12+b12*trans[2]|a21+b21*trans[1];
 
        //avec[.,i]=a12+b12*trans[2]+b122*trans[2]^2+b123*trans[2]^3|a21+b21*trans[1]+b212*trans[1]^2+b213*trans[1]^3;
 
        // Structural coefficients matrix at time i
 
        A=1~avec[1,i]|avec[2,i]~1;
 
        // Structural Shocks
 
        epsilon[.,i]=A*y[i,.]';
 
        // Log-Likelihood
 
        var=(1~rho*sqrt(exp(lnvar[1,i]+lnvar[2,i]))/exp(lnvar[2,i])|rho*sqrt(exp(lnvar[1,i]+lnvar[2,i]))/exp(lnvar[1,i])~1)*diagrv(eye(k),exp(lnvar[.,i]));
 
        llik[i]=-0.5*(k*ln(2*pi)+ln(det(var))-2*ln(det(A))+y[i,.]*(A'*inv(var)*A)*y[i,.]');
 
        i=i+1;
 
    endo;
 
    struct modelResults mm;
 
    mm.Function=llik;
 
    retp(mm);
 
endp;
 
// Inequality restrictions
 
proc ineqp(struct PV p, struct DS d0);
 
    local g,d,f;
 
    g=pvUnpack(p,"g");
 
    d=pvUnpack(p,"d");
 
    f=pvUnpack(p,"f");
 
    retp(1-g[1,1]|1-g[2,2]|d[1,1]|d[2,2]);
 
endp;
 
c0.IneqProc=&ineqp;
 
// Equality restrictions ... restrict spillover from 1 to 2 (1 to source) to zero
 
proc eqp(struct PV p, struct DS d0);
 
    local g,d,f,b12,b122,b123,b21,b212,b213;
 
    b21=pvUnpack(p,"b21");
 
    b212=pvUnpack(p,"b212");
 
    b213=pvUnpack(p,"b213");
 
    b12=pvUnpack(p,"b12");
 
    b122=pvUnpack(p,"b122");
 
    b123=pvUnpack(p,"b123");
 
    g=pvUnpack(p,"g");
 
    d=pvUnpack(p,"d");
 
    f=pvUnpack(p,"f");
 
    retp(b212|b213|b122|b123); // adjust to desired rstrictions
 
endp;
 
//c0.eqProc=&eqp; // uncomment for activating equality restrictions
 
// Output
 
struct cmlmtResults res;
 
res=cmlmt(&likproc,p,d0,c0);
 
cmlmtprt(res);

If I want to get covariance matrix after some certain amount of iteration even if cmlmt doesn't  get normal convergence. for example, after 100 iterations I want to print out the covpar member of the cmlmtResults structure .

please help me