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st: Does ml requires a non-linear function to have a linear part?


From   "Miguel Angel Duran" <maduran@uma.es>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Does ml requires a non-linear function to have a linear part?
Date   Fri, 26 Apr 2013 14:30:51 +0200

In all the examples that I have been able to find about how to use ml to
estimate a non-linear equation, there is always a linear part that makes it
possible to specify the dependent variable. Nevertheless, the equation I am
trying to estimate does not have that linear part. Can anyone help me to
know whether I can use ml (and how if it were possible)?

Just to explain myself beter, this is my equation,

vdmean = b*vlagmean^c*(1-vlagmean)  

And this is one of the things what I have tried to do,

. program datos4mean
  1. version 10.1
  2. args lnf theta2 theta3 sigma
  3. quietly replace `lnf' = ln(normalden($ML_y1, `theta2' *
vlagmean^`theta3' * (1-vlagmean), `sigma'))
  4. end

. ml model lf datos4mean (vdmean=mlagmean, nocons) (theta2:) (theta3:)
(sigma:), vce(robust)

. ml check

RESULT: datos3mean HAS PASSED ALL TESTS

. ml maximize

And I get this message,

initial:       log pseudolikelihood = -72.946848
rescale:       log pseudolikelihood =  219.01781
rescale eq:    log pseudolikelihood =  219.52686
could not calculate numerical derivatives
flat or discontinuous region encountered
r(430);


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