I am not an -ml- expert, but I sense some basic
misunderstandings.
A positive likelihood is not problematic as such.
Although a probability cannot exceed 1,
this is not true of a probability density. Whenever
probability densities are typically above 1, their product
will be too.
However, your program calculates the likelihood directly,
before logging it, and the numbers become far too big to handle.
exp(35000) is big!!!
Thus it is the likelihood that becomes missing, not your
original data. Stata would just ignore any data missings.
You must rewrite your program so that you
are working in terms of the log likelihood throughout.
A side-issue with your program is that you appear to
be putting lots of constants into variables. Using
scalars is much better style and less wasteful
of storage.
Nick
[email protected]
DEEPANKAR BASU
>
> 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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