CMLMT function evaluation failed

I wrote a program try to utilize the frame of the 2011 paper  "Markov-Switching GARCH models in finance: a unifying framework with an application to the German stock market" by Gerrit Reher and Bernd Wilfling.

GAUSS replys as follows:

"CMLMT function evaluation failed".

...\src\cmlmt.src(517) : error G0474 : ' cmlmt(llProc, struct PV par1, struct DS data1, struct cmlmtControl c1)' : Illegal creation of global
...\src\cmlmt.src(521) : error G0017 : 'local s,h,ky,old,f0,iter, np,k1,k2,numeq,qp_m,t0,lbl,s0,s1,s2,m0,c0,qp_t,qp_xl,q' : WARNING: LOCAL outside of procedure
...\src\cmlmt.src(530) : error G0474 : 'data1' : Illegal creation of global

Could anyone tell me what does the error message? Great Thanks!

My program is posted as follows:

library cmlmt;

#include cmlmt.sdf;

// load data
r1day1 = xlsReadM("SHIBOR.xlsx","b2:b2059",1,0);
r1day2 = xlsReadM("SHIBOR.xlsx","b3:b2060",1,0);

struct DS d0;
// create a default data structure
d0 = reshape(dsCreate,2,1);
d0[1].datamatrix = r1day1;
d0[2].datamatrix = r1day2;

struct PV p0;
// creates a default parameter structure
p0 = pvCreate;
p0 = pvPackm(p0,1|2,"meanR1",1|1);
// mean parameters in regime 1, arranged as a1,b1
p0 = pvPackm(p0,5|4,"meanR2",1|1);
// mean parameters in regime 2, arranged as a2,b2
p0 = pvPackm(p0,0|1|1|1|1,"varR1",1|1|1|1|1);
/* variance parameters in regime1, arranged as
omega1,alpha1,beta1,mu1,v1 */
p0 = pvPackm(p0,0|1|1|1|1,"varR2",1|1|1|1|1);
/* variance parameters in regime2, arranged as
omega2,alpha2,beta2,mu2,v2 */
p0 = pvPackm(p0,1|1,"TP",1|1);
// parameters for transition matrix, e1,e2
// p11 = 1/(1+exp(e1)), p22 = 1/(1+exp(e2))
p0 = pvPackm(p0,1|1,"hstart",1|1);
// parameters for starting value of h(1,0)&h(2,0)
p0 = pvPackm(p0,1|1,"hf1",1|1);
// parameters for calculating f(delata) of regime 1
p0 = pvPackm(p0,1|1,"hf2",1|1);
// parameters for calculating f(delata) of regime 2

struct cmlmtControl c0;
// creates a default structure
c0 = cmlmtControlCreate;
c0.Bounds = { -50 50, -50 50,
-50 50, -50 50,
-10 10, .0001 50, .0001 50, .0001 50, .0001 50,
-10 10, .0001 50, .0001 50, .0001 50, .0001 50,
-50 50, -50 50,
.0001 10, .0001 10,
-5 5, -5 5,
-5 5, -5 5 };
c0.NumObs = 2058;

// prints the results
call cmlmtprt(cmlmt(&rs_garch,p0,d0,c0));

proc rs_garch(struct pv p, struct ds d, ind);
local meanR1,meanR2,varR1,varR2,TP,hstart,hf1,hf2,
p11,p22,p1,rowN,ex1,ex2,r1dayF,r1dayL,
fvector,pvector,pFvector,pIvector,hvector,hIvector,dIvector,tfvector;
struct modelResults mm;

meanR1 = pvUnpack(p,"meanR1");
/* a1 = meanR1[1];
b1 = meanR1[2]; */
meanR2 = pvUnpack(p,"meanR2");
/* a2 = meanR2[1];
b2 = meanR2[2]; */
varR1 = pvUnpack(p,"varR1");
/* omega1 = varR1[1];
alpha1 = varR1[2];
beta1 = varR1[3];
mu1 = varR1[4];
v1 = varR1[5]; */
varR2 = pvUnpack(p,"varR2");
/* omega2 = varR2[1];
alpha2 = varR2[2];
beta2 = varR2[3];
mu2 = varR2[4];
v2 = varR2[5]; */
TP = pvUnpack(p,"TP");
/* e1 = TP[1];
e2 = TP[2]; */
hstart = pvUnpack(p,"hstart");
/* h1_0 = hstart[1];
h2_0 = hstart[2]; */
hf1 = pvUnpack(p,"hf1");
/* c1 = hf1[1];
d1 = hf1[2]; */
hf2 = pvUnpack(p,"hf2");
/* c2 = hf2[1];
d2 = hf2[2]; */

p11 = 1/(1+exp(TP[1]));
P22 = 1/(1+exp(TP[2]));
p1 = (1-p22)/(2-p11-p22); // invariant probability of being in regime 1

r1dayF = d[1].datamatrix;
r1dayL = d[2].datamatrix;

rowN = rows(r1dayL);

if ind[1];

fvector = zeros(rowN,2); // a matrix consists of [f(r(t+1)|r(t~),s(t)=1),f(r(t+1)|r(t~),s(t)=2)]
pvector = zeros(rowN,2); // a vector consists of [p(s(t)=1|r(t~)),p(s(t)=2|r(t~))]
pIvector = zeros(rowN,2); // a matrix consists of [p1(1,t),p2(1,t)]
pFvector = zeros(rowN,2); // a matrix consists of [p(s(t-1)=1|r(t~)),p(s(t-1)=2|r(t~))]
hvector = zeros(rowN,2); // a matrix consists of [h(1,t),h(2,t)]
hIvector = zeros(rowN,2); // a matrix consists of [hi(1,t),hi(2,t)]
dIvector = zeros(rowN,2); // a matrix consists of [delta_i(1,t),delta_i(2,t)]
tfvector = zeros(rowN,1); // a vector consists of f(r(t+1)|r(t~))

ex1 = r1dayF-(r1dayL+(meanR1[1]+meanR1[2]*r1dayL)/250);
ex2 = r1dayF-(r1dayL+(meanR2[1]+meanR2[2]*r1dayL)/250);

fvector[1,1] = exp(-ex1[1]^2/(2*hstart[1]))/(2*pi*hstart[1])^.5;
fvector[1,2] = exp(-ex2[1]^2/(2*hstart[2]))/(2*pi*hstart[2])^.5;

pvector[1,.] = p1~(1-p1);
pFvector[1,.] = pvector[1,.].*fvector[1,.]./sumr(pvector[1,.].*fvector[1,.]);
pIvector[1,1] = p11*pvector[1,1]/(p11*pvector[1,1]+(1-p22)*pvector[1,2]);
pIvector[1,2] = (1-p11)*pvector[1,1]/((1-p11)*pvector[1,1]+p22*pvector[1,2]);
hvector[1,.] = hstart';
hIvector[1,1] = pIvector[1,1]*hvector[1,1]+(1-pIvector[1,1])*hvector[1,2]
+pIvector[1,1]*(1-pIvector[1,1])*(ex1[1]-ex2[1])^2;
hIvector[1,2] = pIvector[1,2]*hvector[1,1]+(1-pIvector[1,2])*hvector[1,2]
+pIvector[1,2]*(1-pIvector[1,2])*(ex1[1]-ex2[1])^2;
dIvector[1,1] = pIvector[1,1]*ex1[1]/hvector[1,1]^.5+(1-pIvector[1,1])*ex2[1]/hvector[1,2]^.5;
dIvector[1,2] = pIvector[1,2]*ex1[1]/hvector[1,1]^.5+(1-pIvector[1,2])*ex2[1]/hvector[1,2]^.5;

tfvector[1] = sumr(fvector[1,.].*pvector[1,.]);

for i (2,rowN,1);
hvector[i,1] = (varR1[1]+varR1[2]*(hIvector[i-1,1])^(varR1[4]/2)*(abs(dIvector[i-1,1]-hf1[2])-hf1[1]*(dIvector[i-1,1]-hf1[2]))^varR1[5]
+varR1[3]*(hIvector[i-1,1])^(varR1[4]/2))^(2/varR1[4]);
hvector[i,2] = (varR2[1]+varR2[2]*(hIvector[i-1,2])^(varR2[4]/2)*(abs(dIvector[i-1,2]-hf2[2])-hf2[1]*(dIvector[i-1,2]-hf2[2]))^varR2[5]
+varR2[3]*(hIvector[i-1,2])^(varR2[4]/2))^(2/varR2[4]);

fvector[i,1] = exp(-ex1[i]^2/(2*hvector[i,1]))/(2*pi*hvector[i,1])^.5;
fvector[i,2] = exp(-ex2[i]^2/(2*hvector[i,2]))/(2*pi*hvector[i,2])^.5;

pvector[i,1] = p11*pFvector[i-1,1]+(1-p22)*pFvector[i-1,2];
pvector[i,2] = p22*pFvector[i-1,2]+(1-p11)*pFvector[i-1,1];

pIvector[i,1] = p11*pvector[i,1]/(p11*pvector[i,1]+(1-p22)*pvector[i,2]);
pIvector[i,2] = (1-p11)*pvector[i,1]/((1-p11)*pvector[i,1]+p22*pvector[i,2]);

hIvector[i,1] = pIvector[i,1]*hvector[i,1]+(1-pIvector[i,1])*hvector[i,2]
+pIvector[i,1]*(1-pIvector[i,1])*(ex1[1]-ex2[1])^2;
hIvector[i,2] = pIvector[i,2]*hvector[i,1]+(1-pIvector[i,2])*hvector[i,2]
+pIvector[i,2]*(1-pIvector[i,2])*(ex1[1]-ex2[1])^2;
dIvector[i,1] = pIvector[i,1]*ex1[1]/hvector[i,1]^.5+(1-pIvector[i,1])*ex2[1]/hvector[i,2]^.5;
dIvector[i,2] = pIvector[i,2]*ex1[1]/hvector[i,1]^.5+(1-pIvector[i,2])*ex2[1]/hvector[i,2]^.5;

tfvector[i] = sumr(fvector[i,.].*pvector[i,.]);

endfor;

mm.function = sumc(ln(tfvector));
endif;
retp(mm);
endp;

 

2 Answers



0



accepted

The error "CMLMT : function evaluation failed", means that when CMLMT called your function, the return value was something invalid, like a NaN or missing value.

I don't have your data, so it could be different, but when I plugged in some random normal inputs, I saw that at the third iteration of the for loop inside of rs_garch, pIvector is being assigned missing values. This appears to be happening, because the i'th row of pvector is zeros at that point, so the line:

pIvector[i,1] = p11*pvector[i,1]/(p11*pvector[i,1]+(1-p22)*pvector[i,2]);

is dividing by zero. Maybe that row of pvector should have already been assigned to, or maybe your algorithm should reference, i - 1 of pvector. Without looking in depth at your algorithm, it is hard to say, but this should give you a very good start towards finding the problem in your likelihood function.

aptech

1,323


0



Thanks a lot.

After cheched the for loop, I found that the calculation for pFvector is missing inside the loop, which lead to the dividing by zero after looping about three times.

The corrected program run well though giving out a poor result.

Using the cmlmt library, can I solve any problem if I could provide the related loglikelihood procedure?

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