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st: problems wih stata maximize


From   erik.b.brouwer@nl.pwcglobal.com
To   statalist@hsphsun2.harvard.edu
Subject   st: problems wih stata maximize
Date   Fri, 14 Feb 2003 13:44:44 +0100

Dear All

I have a question about maximize. If I type over the regression example in
the book about maximum likelihood of Stata (page 25) I will get a different
answer then using the inbuild function Reg in Stata. Below you can see the
outcome and programming of maximize and the outcome using reg. What do I do
wrong? I will program a complicated heckman model, but before I start I
want to know what I do wrong?

Regards,

Erik Brouwer


. program define myreg;
  1.         version 6;
  2.         args lnf theta1 theta2;
  3.         quietly replace `lnf'=ln(normd(($ML_y1-`theta1')/`theta2'))-ln
(`theta2');
  4. end;

. ml model lf myreg (employ96=patent96 partne96) ();

. **ml check;
. **ml init eq1:_cons=120 eq2:_cons=704;
. **ml init       66.48697        167.9247        119.9818   704.13,copy;
. ml maximize;

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -34155.307
rescale:       log likelihood = -34155.307
rescale eq:    log likelihood = -31302.083
Iteration 0:   log likelihood = -31302.083
Iteration 1:   log likelihood = -30754.718
Iteration 2:   log likelihood = -30742.076
Iteration 3:   log likelihood =  -30741.23
Iteration 4:   log likelihood =  -30741.13  (backed up)
Iteration 5:   log likelihood = -30741.105  (backed up)
Iteration 6:   log likelihood = -30741.093  (backed up)
Iteration 7:   log likelihood = -30741.087  (backed up)
numerical derivatives are approximate
nearby values are missing
numerical derivatives are approximate
nearby values are missing
Iteration 8:   log likelihood = -30741.084  (backed up)

                                                  Number of obs   =
3853
                                                  Wald chi2(2)    =
35.58
Log likelihood = -30741.084                       Prob > chi2     =
0.0000

------------------------------------------------------------------------------
    employ96 |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+----------------------------------------------------------------
eq1          |
    patent96 |   54.18778   30.66349     1.77   0.077     -5.91155
114.2871
    partne96 |     136.86   26.63454     5.14   0.000     84.65728
189.0628
       _cons |   132.7101   14.99855     8.85   0.000     103.3135
162.1067
-------------+----------------------------------------------------------------
eq2          |
       _cons |   742.3306   9.178238    80.88   0.000     724.3416
760.3196
------------------------------------------------------------------------------

. reg employ96 patent96 partne96;

      Source |       SS       df       MS              Number of obs =
3853
-------------+------------------------------           F(  2,  3850) =
29.77
       Model |  29523053.7     2  14761526.9           Prob > F      =
0.0000
    Residual |  1.9088e+09  3850  495793.554           R-squared     =
0.0152
-------------+------------------------------           Adj R-squared =
0.0147
       Total |  1.9383e+09  3852  503200.477           Root MSE      =
704.13

------------------------------------------------------------------------------
    employ96 |      Coef.   Std. Err.      t    P>|t|     [95% Conf.
Interval]
-------------+----------------------------------------------------------------
    patent96 |   66.48697    29.0777     2.29   0.022     9.477817
123.4961
    partne96 |   167.9247   25.26359     6.65   0.000     118.3934
217.456
       _cons |   119.9818   14.22229     8.44   0.000     92.09783
147.8657
------------------------------------------------------------------------------

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