Hi all,
I tried to follow the following post (http://www.stata.com/statalist/archi.../msg01213.html) to solve for 2 unknows. However, Mata did not work.
I get this error code:
Could someone tell me the problem?
Here is the code I used (following the link above):
I also provide a part of my data
I tried to follow the following post (http://www.stata.com/statalist/archi.../msg01213.html) to solve for 2 unknows. However, Mata did not work.
I get this error code:
numerical derivatives are approximate
flat or discontinuous region encountered
Iteration 0: f(p) = 1.029e+13 (not concave)
could not calculate numerical derivatives -- discontinuous region with missing values
encountered
could not calculate numerical derivatives -- discontinuous region with missing values
encountered
r(430);
flat or discontinuous region encountered
Iteration 0: f(p) = 1.029e+13 (not concave)
could not calculate numerical derivatives -- discontinuous region with missing values
encountered
could not calculate numerical derivatives -- discontinuous region with missing values
encountered
r(430);
Here is the code I used (following the link above):
Code:
mata
mata clear
j=1
T = st_data(j,"time")
sqrtT = sqrt(T)
rf= st_data(j,"r") /*risk free rate*/
F = st_data(j, "x") /*debt*/
E = st_data(j, "ve") /*Equity*/
Evol = st_data(j, "sige") /*Volatility of equity*/
Avol = Evol/2 /*Asset volatility*/
Avalue = (E + F)
c = (sqrtT, rf, E, Evol, F)
void Merton(todo, A, c, lnf, g, H)
{
d1=((ln(A[1]/c[5]))+(c[2]+0.5*(A[2]^2)*c[1]^2))/(A[2]*c[1])
d2=d1-A[2]*c[1]
f1=A[1]*normal(d1)- exp(-c[2]*c[1]^2)*c[5]*normal(d2)-c[3]
f2=(A[1]/c[3])*normal(d1)*A[2]-c[4]
lnf =(f1)^2 + (f2)^2
}
while (j<=st_nobs()){
if (T!=. & rf!=. & F!=. & E!=. & Evol!=.) {
S = optimize_init()
optimize_init_evaluator(S, &Merton())
optimize_init_evaluatortype(S, "d0")
optimize_init_params(S, (Avalue,Avol))
optimize_init_which(S, "min")
optimize_init_argument(S,1,c)
A = optimize(S)
st_store(j, "va", A[1])
st_store(j, "siga", A[2])
j++
if (j<=st_nobs()){
T = st_data(j,"time")
sqrtT = sqrt(T)
rf= st_data(j,"r")
F = st_data (j, "x")
E = st_data (j, "ve")
Evol = st_data (j, "sige")
Avol = Evol/2
Avalue = (E + F)
c = (sqrtT, rf, E, Evol, F)
}
}
else {
j++
if (j<=st_nobs()){
T = st_data(j,"time")
sqrtT = sqrt(T)
rf= st_data(j,"r")
F = st_data (j, "x")
E = st_data (j, "ve")
Evol = st_data (j, "sige")
Avol = Evol/2
Avalue = (E + F)
c = (sqrtT, rf, E, Evol, F)
}
}
}
end
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input float(id quarter ve x va sige siga time r) 29 107 156015 3217961 3373976 .2379536 .01100314 1 5.78 252 107 666777.9 14623456 15290234 .15519053 .006767562 1 5.78 36 107 16589.25 177392 193981.25 .1666332 .01425045 1 5.78 352 107 1115694 20235900 21351594 .28725544 .01501008 1 5.78 52 107 14706 208718 223424 .3383645 .022271506 1 5.78 304 107 205669.5 2213467 2419136.5 .3440633 .029251486 1 5.78 17 107 2253376 100151000 102404376 .6424432 .01413676 1 5.78 210 107 682592.8 11802700 12485293 .19676542 .01075751 1 5.78 255 107 118261 2071609 2189870 .534631 .028872034 1 5.78 51 107 7254 153226 160480 .6672292 .030160025 1 5.78 12 107 2681256 59731156 62412412 .1567459 .00673385 1 5.78 213 107 511008.75 5560495 6071504 .1920298 .016162207 1 5.78 258 107 427917 5432689 5860606 .1897896 .013857645 1 5.78 257 107 223184 2017193 2240377 .2317123 .023082934 1 5.78 11 107 277163.25 4728359 5005522 .2698155 .014940088 1 5.78 351 107 1447675 26594912 28042588 .1726921 .008915085 1 5.78 30 107 14007.5 166464 180471.5 .6742408 .05233197 1 5.78 7 107 565389.5 6306171 6871561 .2142143 .017625475 1 5.78 143 107 584993.5 3152067 3737060.5 .17075734 .02673008 1 5.78 281 107 102225 872728 974953 .3628784 .03804824 1 5.78 474 107 448812 7659290 8108102 .16709583 .009249343 1 5.78 15 107 277655.75 3422593 3700249 .2908917 .021827655 1 5.78 243 107 27829 359649 387478 .20803265 .014941082 1 5.78 481 107 426330 4813621 5239951 .1843567 .01499953 1 5.78 435 107 1452733 16219592 17672324 .10972014 .009019417 1 5.78 136 107 1248167 13226869 14475036 .16186854 .013957753 1 5.78 219 107 230974 2350453 2581427 .5296666 .04739209 1 5.78 3 107 578070.5 30760992 31339062 .4605464 .008495094 1 5.78 256 107 434448 6140086 6574534 .22234038 .014692347 1 5.78 140 107 428750 4045679 4474429 .2242697 .021490036 1 5.78 285 107 144586.75 3169062 3313649 .4308943 .01880151 1 5.78 21 107 376605.5 2980117 3356722.5 .3518122 .03947137 1 5.78 55 107 139468 1009128 1148596 .2419881 .02938335 1 5.78 216 107 409838 1359806 1769644 .8994959 .2083174 1 5.78 168 107 439702.9 5580319 6020022 .3094057 .02259902 1 5.78 26 107 31421.5 483614 515035.5 .45161 .02755201 1 5.78 470 107 450899.25 5196205 5647104 .1730482 .013817223 1 5.78 307 107 1450967 21320772 22771740 .18380323 .011711553 1 5.78 171 107 475682.75 4431634 4907317 .24364486 .02361732 1 5.78 208 107 1655656.3 19195596 20851252 .2329672 .018498342 1 5.78 66 107 7845.5 112477 120322.5 .14465873 .009432318 1 5.78 471 107 499913.75 7364255 7864169 .13235593 .008413673 1 5.78 81 107 1896017 70631000 72527016 .14298674 .003737991 1 5.78 138 107 2715116 25279808 27994924 .15011474 .014559031 1 5.78 181 107 408944.25 5920820 6329764 .2046095 .013219115 1 5.78 354 107 49577.75 517769 567346.75 .2185538 .01909838 1 5.78 318 107 1401400 13648643 15050043 .15599944 .014526046 1 5.78 20 107 2723346.5 42234400 44957748 .16622637 .010069277 1 5.78 28 107 646930.1 10158831 10805761 .16921726 .010130868 1 5.78 263 107 222546 2430035 2652581 .21503285 .018040806 1 5.78 173 107 2657369 25255164 27912532 .1785632 .016999831 1 5.78 172 107 721400 8971612 9693012 .13001361 .00967623 1 5.78 167 107 767894.3 8847793 9615687 .2367543 .01890684 1 5.78 217 107 362631.25 4032297 4394928 .3216273 .026537884 1 5.78 33 107 330225 3048914 3379139 .2574757 .02516171 1 5.78 224 107 161438.75 3438277 3599716 .3670127 .016459651 1 5.78 130 107 31500 3426505 3458005 .6199373 .005647194 1 5.78 300 107 557100 7207298 7764398 .1726348 .012386644 1 5.78 215 107 611478 5557419 6168897 .1673322 .016586425 1 5.78 419 107 120950 1086859 1207809 .36213875 .036264576 1 5.78 24 107 10323.5 195830 206153.5 .8793883 .04403692 1 5.78 119 107 112477.5 2445150 2557627.5 .4515339 .019857235 1 5.78 35 107 24340 234498 258838 .53807527 .05059826 1 5.78 467 107 583627.6 8594064 9177692 .2155595 .013707856 1 5.78 132 107 596520 7246920 7843440 .2475891 .018829985 1 5.78 462 107 457872 6623391 7081263 .2306818 .014915806 1 5.78 10 107 2444780 52661988 55106768 .22596245 .010024694 1 5.78 88 107 2880067.5 89883520 92763584 .26011217 .008075804 1 5.78 234 107 3478.125 143534 147012.13 .1591195 .003764571 1 5.78 118 107 355936.5 4951378 5307315 .15332127 .010282532 1 5.78 306 107 1195933.5 10919692 12115626 .1602834 .015821574 1 5.78 223 107 259175 2798318 3057493 .20509467 .017385293 1 5.78 131 107 632517.5 8493690 9126208 .13318835 .009230994 1 5.78 232 107 203362.5 838757 1042119.5 .6223659 .12145045 1 5.78 254 107 141787 1285280 1427067 .26094636 .025926465 1 5.78 77 107 3176188 53698644 56874832 .2297719 .012831664 1 5.78 1 107 450933.25 4596164 5047097 .13559069 .01211436 1 5.78 225 107 108647.25 1035460 1144107.3 .26390937 .025061486 1 5.78 174 107 1713614.5 26163570 27877184 .27529234 .01692226 1 5.78 214 107 379764.25 3543081 3922845 .28074995 .027178945 1 5.78 468 107 343685.25 3614569 3958254 .3819469 .033163484 1 5.78 305 107 1695485 32276100 33971584 .27038696 .01349472 1 5.78 4 107 1584700 16328863 17913564 .15386647 .013611597 1 5.78 78 107 1166016.8 19656332 20822348 .2071956 .011602607 1 5.78 272 107 107010 597080 704090 .3012076 .04577855 1 5.78 221 107 378810.25 3199263 3578073 .227398 .0240746 1 5.78 368 107 181520.88 4420669 4602190 .3450024 .013607684 1 5.78 89 107 7293595 187064000 194357600 .2103857 .007895076 1 5.78 273 107 234148.5 1211189 1445337.5 .29162902 .04724467 1 5.78 84 107 7340850 70908504 78249352 .17887387 .016780794 1 5.78 270 107 98091 1030902 1128993 .5040353 .04379241 1 5.78 19 107 223842.75 3097537 3321380 .18835774 .012694277 1 5.78 137 107 147805 2348109 2495914 .4005369 .023719307 1 5.78 469 107 401184 7352823 7754007 .1525159 .00789101 1 5.78 211 107 36224.375 260018 296242.38 .11759332 .014379255 1 5.78 212 107 703526.1 4007575 4711101 .2332856 .034837402 1 5.78 86 107 85190 535826 621016 .3718926 .05101564 1 5.78 260 107 125510.25 1647974 1773484.3 .316285 .02238363 1 5.78 402 107 1952828.3 17512120 19464948 .18991856 .01905365 1 5.78 276 107 102543 523980 626523 .26249704 .04296288 1 5.78 end format %tq quarter