Hi Statalisters,
I am facing problems while trying to do a maximum likelihood estimation. I have written out the likelihood estimator; then I have used "ml check". The "ml check" shows that my likelihood evaluator has passed all tests. Then when I issue the "ml max" command, I get the following error message:
initial: log likelihood = 31058.016
rescale: log likelihood = 31058.016
rescale eq: log likelihood = 34639.752
(setting optimization to BHHH)
could not calculate numerical derivatives
missing values encountered
r(430);
First thing that worries me is that the log likelihood is positive; is that a sign that my program has some problem? And second, the maximisation is interrupted before completion because it could not calculate numerical derivatives (missing values encountered). I re-checked my dataset; there are no missing values for the variables that I am using. Probably the error message means something else.
I have tried (1) switching between techniques BHHH and NR; (2) ml max, difficult. I still get the same error message (though the log likelihood values where the execution of the program stops are different in each case).
Below I give the do-file that has my likelihood evaluator program and also a "ml model ..." command (as suggested in the book by Gould, Pitbaldo and Sribney: page 165); any suggestions are welcome.
If someone can detect some error immediately, that will be great. But, if someone needs to run the program, I can send a small subset of my dataset for the purpose. Thanks in advance.
Deepankar Basu
____________________________________________
clear
capture program drop gender_lf
program gender_lf
version 8.1
set mem 100m
#delimit ;
args lnf theta1 theta2 theta3
theta5 theta6 theta7 theta8 theta9
theta10 theta11 theta12 theta13 theta14;
tempvar q1 q2 q3 q4 q5 q6 q7 q8 q9 q10 q11 q12 q13 q14
x x1 x2 x3 x4 x5 x6 x7;
#delimit cr
quietly {
gen double `q1' = 1/(1+exp(`theta5'))
gen double `q2' = exp(`theta5')/(1+exp(`theta5'))
gen double `q3' = 1/(1+exp(`theta6')+exp(`theta7'))
gen double `q4' = exp(`theta6')/(1+exp(`theta6')+exp(`theta7'))
gen double `q5' = exp(`theta7')/(1+exp(`theta6')+exp(`theta7'))
gen double `q6' = 1/(1+exp(`theta8')+exp(`theta9')+exp(`theta10'))
gen double `q7' = exp(`theta8')/(1+exp(`theta8')+exp(`theta9')+exp(`theta10'))
gen double `q8' = exp(`theta9')/(1+exp(`theta8')+exp(`theta9')+exp(`theta10'))
gen double `q9' = exp(`theta10')/(1+exp(`theta8')+exp(`theta9')+exp(`theta10'))
gen double `q10' = 1/(1+exp(`theta11')+exp(`theta12')+exp(`theta13')+ exp(`theta14'))
gen double `q11' = exp(`theta11')/(1+exp(`theta11')+exp(`theta12')+exp(`theta13')+ exp(`theta14'))
gen double `q12' = exp(`theta12')/(1+exp(`theta11')+exp(`theta12')+exp(`theta13')+ exp(`theta14'))
gen double `q13' = exp(`theta13')/(1+exp(`theta11')+exp(`theta12')+exp(`theta13')+ exp(`theta14'))
gen double `q14' = exp(`theta14')/(1+exp(`theta11')+exp(`theta12')+exp(`theta13')+ exp(`theta14'))
gen double `x1' = 2^(-$ML_y1)
gen double `x2' = exp(lnfact($ML_y1))
gen double `x3' = exp(`theta1')
gen double `x4' = exp($ML_y1*`theta1')
gen double `x5' = norm(-`theta3')
gen double `x6' = exp(`theta2')
gen double `x7' = exp($ML_y1*`theta2')
#delimit ;
gen double `x' = `x1'*(1/`x2')*exp(-`x3')*`x4'*`x5' + (1-`x5')*exp(-`x6')*`x7' +
$ML_y2 + ($ML_y3*`q1' + $ML_y4*`q2') +
($ML_y5*`q3' + $ML_y6*`q4'+ $ML_y7*`q5') +
($ML_y8*`q6' + $ML_y9*`q7'+ $ML_y10*`q8'+$ML_y11*`q9') +
($ML_y12*`q10' + $ML_y13*`q11' + $ML_y14*`q12'+ $ML_y15*`q13'+$ML_y16*`q14');
#delimit cr
}
quietly replace `lnf' = ln(`x')
end
use india1.dta
#delimit ;
ml model lf gender_lf (one: dfsize p21 p31 p32 = v012 v025, nocons)
(two: p41 p42 p43 p51 p52 p53 p54 = v012 v025)
(three: p61 p62 p63 p64 p65 = v012 v025 v130, nocons)
(four:) (five:) (six:) (sev:) (eight:) (nine:) (ten:) (eleven:) (twelve:) (thirteen:), tech(bhhh nr);
#delimit cr
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