input parameter in program

We need more information to provide a meaningful answer. We need to see how the variable prmtr_in is used. If you can show more code, that will be helpful. We may just need to see the objective function code.

this is all my program, 13 parameters on "prmtr_in"  included in my program is obtained from where? Thank

 

new;
library optmum,PGRAPH;
optset;
_opstmth = "bfgs stepbt";

format /m1 /rd 9,5;

load y[3888,2] = C:\gausslt14\log_return.txt;
yy_d=y[.,1];

t0=rows(yy_d);
LAG_AR=1;
NO_ST=LAG_AR+1; @ NUMBER OF STATES TO BE CONSIDERED@
DMNSION=2^NO_ST;

 

st_mat=zeros(DMNSION,NO_ST);

j=1;

st1=0; do until st1>1;
st=0; do until st>1;

st_mat[j,.]=st1~st;

j=j+1;
st=st+1;endo;
st1=st1+1;endo;

yy=yy_d[lag_ar+1:t0,1];

x_mat= yy_d[Lag_ar:T0-1,1] ;

T=rows(yy);
output file=SandrPTF.out reset;
output off;

@================= Initialize Global Variables============@

START=1;

prmtr_in={4.767376 4.987379 0.0024269 0.08740 -0.0020319 -0.0045147
0.0084271 0.0010101 -0.0071791 -0.0010549 0.0016770 -0.0026814 -0.00600 };
PRMTR_IN=PRMTR_IN';

@ Maximum Likelihood Estimation @
@==================================================@

{xout,fout,gout,cout}=optmum(&lik_fcn,PRMTR_in);
PRM_FNL=TRANS(xout); @ Estimated coefficients, constrained@
prm_11=prm_fnl; save prm_11;
output on;

 

 

"==FINAL OUTPUT========================================================";

"initial values of prmtr is";
prmtr_in';
"==============================================================";
"code is--------";;cout;
"likelihood value is ";; -1*fout;
"Estimated parameters are:";
prm_fnl'; " ";
output off;
"Calculating Hessian..... Please be patient!!!!";

SD_fnl = sqrt(diag(invpd(_opfhess)));

/*hout0=hessp(&lik_fcn,xout);
hout0=(hout0+hout0')./2;
hout=inv(hout0);

grdn_fnl=gradfd(&TRANS,xout);
Hsn_fnl=grdn_fnl*hout*grdn_fnl';
SD_fnl =sqrt(diag(Hsn_fnl));*/

waj=prm_fnl./SD_fnl;

@Standard errors of the estimated coefficients@

output on;
"Standard errors of parameters are:";
sd_fnl';
"===============================================================";
"T-Student of parameters are:";
waj';
"===============================================================";
output off;
/* Don't forget to delete the following line when you estimate the
parameters....

xout=prmtr_in; prm_fnl=trans(xout);
*/

 

{pr_tt0,pr_tl0}=FILTER(XOUT); @Pr[S_t=0|Y_t] and Pr[S_t=0|Y_{t-1}]@

smooth0=SMOOTH(pr_tt0,pr_tl0); @Pr[S_t=0|Y_T], Smoothed probabilities@

res=filter1(prmtr_in);

 

/*FLTR=pr_tt0~pr_tl0~smooth0;
format /m1 /rd 8,4;
output file=hmt4_kim.dta reset; FLTR; output off;*/
xy(seqa(1,1,rows(pr_tt0)),pr_tt0);
xy(seqa(1,1,rows(smooth0)),smooth0);
end;

@ end OF MAIN PROGRAM @

 
@========================================================================@
@========================================================================@
proc LIK_FCN(PRMTR1);
local prmtr, ppr,qpr,prob__0,prob__1,QQ, lik, j_iter, pr__0_l,pr__1_l,
F_cast, var_L, pr_val,likv,phi,PSIX,
vecp,st,st1,st2,st3,st4,ST5,ST6,ST7,ST8,ST9,ST10,ST11,ST12,ST13,
pr_tr,pr_trf1,pr_trf,prob__t,prob__,pro_,pr_vl,j,psi1,psi2,var_c,
delta0,DELTA1,MU0,MU1,st_k,st_k1,st_k2,st_k3,st_k4,
f_cast1,f_cast2,PR_VL1,PR_VL2,pr_trf7,pr_trf0,
PR_TRF2,PR_TRF3,PR_TRF4,PR_TRF5,PR_TRF6,psic,psiL,
TMPRY1,TMPRY2,SM_PRL,TMP_P0,SM_PR0,JJJ,MU_MAT,D_MAT,FLT_PRN,
F1,F2,TMP00,TMP0,SM_PR00,prob_dd,VAR0,VAR1,A,EN,var,w0,w1,a10,a11,b0,b1,c0,c1,u0,u1,h0,h1,h_L,h,r0,r1;
PRMTR=TRANS(PRMTR1);

LOCATE 15,1; PRMTR';

/* DEFINE PARAMETERS */

PPR=PRMTR[1,1]; @Pr[St=1/St-1=1]@
QPR=PRMTR[2,1]; @Pr[St=0/St-1=0]@

PHI=PRMTR[3,1];

MU0=PRMTR[4,1]; @ recession, vs. boom@
MU1=PRMTR[5,1]; @ recession, vs. boom@

w0=prmtr[6,1];
w1=prmtr[7,1];

a10=prmtr[8,1];
a11=prmtr[9,1];
b0=prmtr[10,1];
b1=prmtr[11,1];
c0=prmtr[12,1];
c1=prmtr[13,1];
MU_MAT=ST_MAT*MU1 + (ONES(DMNSION,NO_ST)-ST_MAT)*MU0;
/*VAR_L=ST_MAT*VAR1 + (ONES(DMNSION,NO_ST)-ST_MAT)*VAR0;
VAR_L=VAR_L[.,2];*/
/* A MATRIX OF TRANSITION PROBABILITIES */

PR_TR=(QPR~ (1-PPR))|
((1-QPR)~ PPR);

/* INITIALIZING THE FILTER WITH STEADY-STATE PROBABILITIES */

A = (eye(2)-pr_tr)|ones(1,2);
EN=(0|0|1);
PROB__T = INV(A'A)*A'EN; @PR[S_t=0]|PR[S_t=1],
2x1 steady-state PROBABILITIES@

PR_TRF0=VEC(PR_TR);

PR_TRF =PR_TRF0;
PROB__T=VECR(PROB__T~PROB__T);
PROB__=PROB__T;

var = vcx(yy); /*initialisation de ht*/
h0 = zeros(T,1);
h1 = zeros(T,1);
h = zeros(T,1);

r0 = zeros(T,1);
r1 = zeros(T,1); /* vecteur des variances*/
r0[1] = w0 + (a10+b0)*var; /* premi�re variance*/
r1[1] = w1 + (a11+b1)*var;
h0[1] = exp(r0[1]); /* premi�re variance*/
h1[1] = exp(r1[1]);

h[1] = qpr.*(mu0^2+h0[1])+(1-qpr).*(mu1^2+h1[1])-(qpr.*mu0+(1-qpr).*mu1)^2;

u0= zeros(T,1); /*cr�ation des vecteurs des r�sidus*/
u1= zeros(T,1);
u0[1]=YY[1,1]-mu0-X_MAT[1,1]*phi;
u1[1]=YY[1,1]-mu1-X_MAT[1,1]*phi;

LIKV=0.0;
J_ITER=2;
do until J_ITER>T;
F_CAST1=(YY[J_ITER,1]-X_MAT[J_ITER,1]*PHI)*ONES(DMNSION,1)-MU_MAT[.,2];

PROB_DD=PR_TRF .* PROB__;
/* Pr[S_t,S_{t-1},S_{t-2},S_{t-3},S_{t-4} | I(t-1)]*/

r0[J_ITER]= w0 + a10*(abs(u0[J_ITER-1]./SQRT h[J_ITER-1])-SQRT(2./PI))+ b0.*ln(h[J_ITER-1]) + c0.*(u0[J_ITER-1]./SQRT(h[J_ITER-1] ));

h0[J_ITER]=exp(r0[J_ITER]);

r1[J_ITER]= w1 + a11*(abs(u1[J_ITER-1]./SQRT h[J_ITER-1])-SQRT(2./PI))+ b1.*ln(h[J_ITER-1]) + c1.*(u1[J_ITER-1]./SQRT(h[J_ITER-1] ));

h1[J_ITER]=exp(r1[J_ITER]);

h[J_ITER] = qpr.*(mu0^2+h0[J_ITER])+(1-qpr).*(mu1^2+h1[J_ITER])-(qpr.*mu0+(1-qpr).*mu1)^2;

h_L=h0[J_ITER]|h1[J_ITER]|h0[J_ITER]|h1[J_ITER];
PR_VL=(1./SQRT(2.*PI.*h_L)).*EXP(-0.5*F_CAST1.*F_CAST1./h_L).*PROB_DD;
/* 2^5x1 */
/* Joint density of y_t,S_t,..,S_{t-4} given
past information : */

PR_VAL=SUMC(PR_VL); /* f(y_t|I(t-1)), density of y_t given past
information: This is weighted average of
2^5 conditional densities */

LIK=-1*LN(PR_VAL);

PRO_=PR_VL/PR_VAL; /* Pr[S_t,S_{t-1},S_{t-2},S_{t-3},S_{t-4} | I(t-1),y_t]*/
/* Updating of prob. with new information, y_t */
PROB__T=PRO_[1:DMNSION/2,1]+PRO_[DMNSION/2+1:DMNSION,1];
/* Integrate out S_{t-4}: then you get
Pr[S_t, S_{t-1},S_{t-2},S_{t-3}| Y_t] */
/* 2^4x1*/

PROB__=VECR(PROB__T~PROB__T);
/* 2^5x1 */

LIKV = LIKV+LIK;
J_ITER = J_ITER+1;

endo;

LOCATE 2,35;"LIKV=";;LIKV;
retp(LIKV);
endp;
@++++++++++++++++++++++++++++++++++++++++++++++++++++++++@

@++++++++++++++++++++++++++++++++++++++++++++++++++++++++@
proc (2) = FILTER(PRMTR1);
local prmtr, ppr,qpr,prob__0,prob__1,QQ, lik, j_iter, pr__0_l,pr__1_l,
F_cast, var_L, pr_val,likv,phi,PSIX,
vecp,st,st1,st2,st3,st4,ST5,ST6,ST7,ST8,ST9,ST10,ST11,ST12,ST13,
pr_tr,pr_trf1,pr_trf,prob__t,prob__,pro_,pr_vl,j,psi1,psi2,var_c,
delta0,DELTA1,MU0,MU1,st_k,st_k1,st_k2,st_k3,st_k4,
f_cast1,f_cast2,PR_VL1,PR_VL2,pr_trf7,pr_trf0,
PR_TRF2,PR_TRF3,PR_TRF4,PR_TRF5,PR_TRF6,psic,psiL,PR_STL0,PR_STT0,
TMPRY1,TMPRY2,SM_PRL,TMP_P0,SM_PR0,JJJ,MU_MAT,D_MAT,FLT_PRN,
F1,F2,TMP00,TMP0,SM_PR00,prob_dd,VAR0,VAR1,TMP,OUT_MAT,A,EN,var,w0,w1,a10,a11,b0,b1,c0,c1,u0,u1,h0,h1,h_L,h,r0,r1;

PRMTR=TRANS(PRMTR1);

LOCATE 15,1; PRMTR';

PPR=PRMTR[1,1]; @Pr[St=1/St-1=1]@
QPR=PRMTR[2,1]; @Pr[St=0/St-1=0]@

PHI=PRMTR[3,1];

MU0=PRMTR[4,1]; @ recession, vs. boom@
MU1=PRMTR[5,1]; @ recession, vs. boom@

w0=prmtr[6,1];
w1=prmtr[7,1];

a10=prmtr[8,1];
a11=prmtr[9,1];
b0=prmtr[10,1];
b1=prmtr[11,1];
c0=prmtr[12,1];
c1=prmtr[13,1];
MU_MAT=ST_MAT*MU1 + (ONES(DMNSION,NO_ST)-ST_MAT)*MU0;
/* A MATRIX OF TRANSITION PROBABILITIES */

PR_TR=(QPR~ (1-PPR))|
((1-QPR)~ PPR);

/* INITIALIZING THE FILTER WITH STEADY-STATE PROBABILITIES */

A = (eye(2)-pr_tr)|ones(1,2);
EN=(0|0|1);
PROB__T = INV(A'A)*A'EN; @PR[S_t=0]|PR[S_t=1],
2x1 steady-state PROBABILITIES@

PR_TRF0=VEC(PR_TR);

PR_TRF =PR_TRF0;
PROB__T=VECR(PROB__T~PROB__T);
PROB__=PROB__T; @2^5 x 1@

var = vcx(yy); /*initialisation de ht*/
h0 = zeros(T,1);
h1 = zeros(T,1);
h = zeros(T,1);

r0 = zeros(T,1);
r1 = zeros(T,1); /* vecteur des variances*/
r0[1] = w0 + (a10+b0)*var; /* premi�re variance*/
r1[1] = w1 + (a11+b1)*var;
h0[1] = exp(r0[1]); /* premi�re variance*/
h1[1] = exp(r1[1]);

h[1] = qpr.*(mu0^2+h0[1])+(1-qpr).*(mu1^2+h1[1])-(qpr.*mu0+(1-qpr).*mu1)^2;

u0= zeros(T,1); /*cr�ation des vecteurs des r�sidus*/
u1= zeros(T,1);
u0[1]=YY[1,1]-mu0-X_MAT[1,1]*phi;
u1[1]=YY[1,1]-mu1-X_MAT[1,1]*phi;

PR_STT0=ZEROS(T,1); @WILL save Pr[S_t=0|Y_{t}@
PR_STL0=ZEROS(T,1); @WILL save Pr[S_t=0|Y_{t-1}@

LIKV=0.0;
J_ITER=2;
do until J_ITER>T;
F_CAST1=(YY[J_ITER,1]-X_MAT[J_ITER,.]*PHI)*ONES(DMNSION,1)-MU_MAT[.,2];

 

PROB_DD=PR_TRF .* PROB__;

@-----------------------------------------------------@
TMP=PROB_DD;

TMP=TMP[1:2]+TMP[3:4];

PR_STL0[J_ITER,1]=TMP[1,1]; @Pr[S_t=0|Y_t]@
@------------------------------------------------------@

r0[J_ITER]= w0 + a10*(abs(u0[J_ITER-1]./SQRT h[J_ITER-1])-SQRT(2./PI))+ b0.*ln(h[J_ITER-1]) + c0.*(u0[J_ITER-1]./SQRT(h[J_ITER-1] ));

h0[J_ITER]=exp(r0[J_ITER]);

r1[J_ITER]= w1 + a11*(abs(u1[J_ITER-1]./SQRT h[J_ITER-1])-SQRT(2./PI))+ b1.*ln(h[J_ITER-1]) + c1.*(u1[J_ITER-1]./SQRT(h[J_ITER-1] ));

h1[J_ITER]=exp(r1[J_ITER]);

h[J_ITER] = qpr.*(mu0^2+h0[J_ITER])+(1-qpr).*(mu1^2+h1[J_ITER])-(qpr.*mu0+(1-qpr).*mu1)^2;

h_L=h0[J_ITER]|h1[J_ITER]|h0[J_ITER]|h1[J_ITER];

PR_VL=(1./SQRT(2.*PI.*h_L)).*EXP(-0.5*F_CAST1.*F_CAST1./h_L).*PROB_DD;

 

PR_VAL=SUMC(PR_VL); @f(y_t| Y_{t-1})@
LIK=-1*LN(PR_VAL);

PRO_=PR_VL/PR_VAL; @Pr[S_t, S_{t-1},S_{t-2},S_{t-3},S_{t-4} | Y_t]@
@-------------------------------------------------------@
TMP=PRO_;

TMP=TMP[1:2]+TMP[3:4];

PR_STT0[J_ITER,1]=TMP[1,1]; @Pr[S_t=0|Y_t]@
@-------------------------------------------------------@

PROB__T=PRO_[1:DMNSION/2,1]+PRO_[DMNSION/2+1:DMNSION,1];
@Pr[S_t, S_{t-1},S_{t-2},S_{t-3}| Y_t]@

PROB__=VECR(PROB__T~PROB__T);
J_ITER = J_ITER+1;
endo;

retp(PR_STT0,PR_STL0);
endp;
@======================================================================@
@======================================================================@
proc SMOOTH(pr_tt0,pr_tl0);

@pr_TT0 contains Pr[S_t|Y_t]@
@pr_TL0 contains Pr[S_t|Y_{t-1}]@
local ppr, qpr, pr_sm0,pr_sm1, j_iter,pr_sm00,pr_sm01,pr_sm10,pr_sm11,
pr_tt1,pr_tl1;

PPR=PRM_fnl[1,1]; @Pr[St=1/St-1=1]@
QPR=PRM_fnl[2,1]; @Pr[St=0/St-1=0]@

pr_tt1=1-pr_tt0;
pr_tl1=1-pr_tl0;

pr_sm0=pr_tt0; @ pr_sm0 will contain Pr[S_t|Y_T]@
pr_sm1=pr_tt1;
j_iter=T-1;
do until j_iter < 1;

@The followings are P[S_t, S_t+1|Y_T] @

pr_sm00=pr_sm0[j_iter+1,1]*qpr* pr_tt0[j_iter,1]/ pr_tl0[j_iter+1,1];

pr_sm01=pr_sm1[j_iter+1,1]*(1-qpr)*pr_tt0[j_iter,1]/ pr_tl1[j_iter+1,1];

pr_sm10=pr_sm0[j_iter+1,1]*(1-ppr)*pr_tt1[j_iter,1]/ pr_tl0[j_iter+1,1];

pr_sm11=pr_sm1[j_iter+1,1]*ppr* pr_tt1[j_iter,1]/ pr_tl1[j_iter+1,1];

pr_sm0[j_iter,1]=pr_sm00+pr_sm01;
pr_sm1[j_iter,1]=pr_sm10+pr_sm11;

j_iter=j_iter -1;
endo;
retp(pr_sm0); @This proc returns Pr[S_t=0|Y_T]@
endp;

 

@++++++++++++++++++++++++++++++++++++++++++++++++++++++++@

proc filter1(PRMTR1);

local prmtr, ppr,qpr,prob__0,prob__1,QQ, lik, j_iter, pr__0_l,pr__1_l,
F_cast, var_L, pr_val,likv,phi,PSIX,
vecp,st,st1,st2,st3,st4,ST5,ST6,ST7,ST8,ST9,ST10,ST11,ST12,ST13,
pr_tr,pr_trf1,pr_trf,prob__t,prob__,pro_,pr_vl,j,psi1,psi2,var_c,
delta0,DELTA1,MU0,MU1,st_k,st_k1,st_k2,st_k3,st_k4,
f_cast1,f_cast2,PR_VL1,PR_VL2,pr_trf7,pr_trf0,
PR_TRF2,PR_TRF3,PR_TRF4,PR_TRF5,PR_TRF6,psic,psiL,
TMPRY1,TMPRY2,SM_PRL,TMP_P0,SM_PR0,JJJ,MU_MAT,D_MAT,FLT_PRN,r,
F1,F2,TMP00,TMP0,SM_PR00,prob_dd,VAR0,VAR1,A,EN,var,w0,w1,a10,a11,b0,b1,c0,c1,u0,u1,h0,h1,h_L,h,r0,r1;
PRMTR=TRANS(PRMTR1);

LOCATE 15,1; PRMTR';

/* DEFINE PARAMETERS */

PPR=PRMTR[1,1]; @Pr[St=1/St-1=1]@
QPR=PRMTR[2,1]; @Pr[St=0/St-1=0]@

PHI=PRMTR[3,1];

MU0=PRMTR[4,1]; @ recession, vs. boom@
MU1=PRMTR[5,1]; @ recession, vs. boom@

w0=prmtr[6,1];
w1=prmtr[7,1];

a10=prmtr[8,1];
a11=prmtr[9,1];
b0=prmtr[10,1];
b1=prmtr[11,1];
c0=prmtr[12,1];
c1=prmtr[13,1];
MU_MAT=ST_MAT*MU1 + (ONES(DMNSION,NO_ST)-ST_MAT)*MU0;
/*VAR_L=ST_MAT*VAR1 + (ONES(DMNSION,NO_ST)-ST_MAT)*VAR0;
VAR_L=VAR_L[.,2];*/
/* A MATRIX OF TRANSITION PROBABILITIES */

PR_TR=(QPR~ (1-PPR))|
((1-QPR)~ PPR);

/* INITIALIZING THE FILTER WITH STEADY-STATE PROBABILITIES */

A = (eye(2)-pr_tr)|ones(1,2);
EN=(0|0|1);
PROB__T = INV(A'A)*A'EN; @PR[S_t=0]|PR[S_t=1],
2x1 steady-state PROBABILITIES@

PR_TRF0=VEC(PR_TR);

PR_TRF =PR_TRF0;
PROB__T=VECR(PROB__T~PROB__T);
PROB__=PROB__T;

var = vcx(yy); /*initialisation de ht*/
h0 = zeros(T,1);
h1 = zeros(T,1);
h = zeros(T,1);

r0 = zeros(T,1);
r1 = zeros(T,1); /* vecteur des variances*/
r0[1] = w0 + (a10+b0)*var; /* premi�re variance*/
r1[1] = w1 + (a11+b1)*var;

 

h0[1] = exp(r0[1]); /* premi�re variance*/
h1[1] = exp(r1[1]);

h[1] = qpr.*(mu0^2+h0[1])+(1-qpr).*(mu1^2+h1[1])-(qpr.*mu0+(1-qpr).*mu1)^2;

u0= zeros(T,1); /*cr�ation des vecteurs des r�sidus*/
u1= zeros(T,1);
u0[1]=YY[1,1]-mu0-X_MAT[1,1]*phi;
u1[1]=YY[1,1]-mu1-X_MAT[1,1]*phi;

r=zeros(T,1);

LIKV=0.0;
J_ITER=2;
do until J_ITER>T;
F_CAST1=(YY[J_ITER,1]-X_MAT[J_ITER,1]*PHI)*ONES(DMNSION,1)-MU_MAT[.,2];

PROB_DD=PR_TRF .* PROB__;
/* Pr[S_t,S_{t-1},S_{t-2},S_{t-3},S_{t-4} | I(t-1)]*/
r[J_ITER,1]=sumc(F_CAST1.*PROB_DD);
r0[J_ITER]= w0 + a10*(abs(u0[J_ITER-1]./SQRT h[J_ITER-1])-SQRT(2./PI))+ b0.*ln(h[J_ITER-1]) + c0.*(u0[J_ITER-1]./SQRT(h[J_ITER-1] ));

h0[J_ITER]=exp(r0[J_ITER]);

r1[J_ITER]= w1 + a11*(abs(u1[J_ITER-1]./SQRT h[J_ITER-1])-SQRT(2./PI))+ b1.*ln(h[J_ITER-1]) + c1.*(u1[J_ITER-1]./SQRT(h[J_ITER-1] ));

h1[J_ITER]=exp(r1[J_ITER]);

h[J_ITER] = qpr.*(mu0^2+h0[J_ITER])+(1-qpr).*(mu1^2+h1[J_ITER])-(qpr.*mu0+(1-qpr).*mu1)^2;

h_L=h0[J_ITER]|h1[J_ITER]|h0[J_ITER]|h1[J_ITER];
PR_VL=(1./SQRT(2.*PI.*h_L)).*EXP(-0.5*F_CAST1.*F_CAST1./h_L).*PROB_DD;
/* 2^5x1 */
/* Joint density of y_t,S_t,..,S_{t-4} given
past information : */

PR_VAL=SUMC(PR_VL); /* f(y_t|I(t-1)), density of y_t given past
information: This is weighted average of
2^5 conditional densities */

LIK=-1*LN(PR_VAL);

PRO_=PR_VL/PR_VAL; /* Pr[S_t,S_{t-1},S_{t-2},S_{t-3},S_{t-4} | I(t-1),y_t]*/
/* Updating of prob. with new information, y_t */
PROB__T=PRO_[1:DMNSION/2,1]+PRO_[DMNSION/2+1:DMNSION,1];
/* Integrate out S_{t-4}: then you get
Pr[S_t, S_{t-1},S_{t-2},S_{t-3}| Y_t] */
/* 2^4x1*/

PROB__=VECR(PROB__T~PROB__T);
/* 2^5x1 */

LIKV = LIKV+LIK;
J_ITER = J_ITER+1;
endo;

LOCATE 2,35;"LIKV=";;LIKV;

retp(r);
endp;

 

@++++++++++++++++++++++++++++++++++++++++++++++++++++++++@

 
proc TRANS(c0); @ constraining values of reg. coeff.@
local c1,m,u,d1,d2,d3,d4,d5,d6;

c1=c0;

c1[1:2,.]=exp(c0[1:2,.])./ (1+exp(c0[1:2,.]));
/*c1[10:11,.]=2*(exp(c0[10:11,.])./ (1+exp(c0[10:11,.])))-1;*/
c1[10:11,.]=c0[10:11,.]./ ( 1 + abs(c0[10:11,.]));
retp(c1);
endp;

 

As is stated in the documentation for the function optmum, the second input to the function optmum is a vector of start values. The start values are the initial parameter values used in the optimization routine.