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st: Constant parameters taken as constant in non-linear estimation


From   "Miguel Angel Duran" <maduran@uma.es>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Constant parameters taken as constant in non-linear estimation
Date   Wed, 24 Apr 2013 10:59:08 +0200

As a result of running the following non-linear model:

nl (vdmean=({b1}+{b2}*vlagmean^{b3})*(1 - vlagmean))

I get the following results:

Iteration 0:  residual SS =  .0050223
Iteration 1:  residual SS =  .0050223

      Source |       SS       df       MS
-------------+------------------------------         Number of obs =
66
       Model | -.000702463     0           .         R-squared     =
-0.1626
    Residual |  .005022252    65  .000077265         Adj R-squared =
-0.1626
-------------+------------------------------         Root MSE      =
.0087901
       Total |  .004319789    65  .000066458         Res. dev.     =
-438.6132

----------------------------------------------------------------------------
--
      vdmean |      Coef.   Std. Err.      t    P>|t|     [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
         /b1 |   .0223499          .        .       .            .
.
         /b2 |   2.14e-17   .0041096     0.00   1.000    -.0082075
.0082075
         /b3 |          0          .        .       .            .
.
----------------------------------------------------------------------------
--
  Parameter b3 taken as constant term in model & ANOVA table

I have seen in a previous message to the Statalist that this results from
the fact that Stata looks for a constant term. But does anyone know whether
there is a way to avoid it? Do you recommend me to estimate the models
through maximum likelihood instead of non-linear LS? By the way, if I
estimate the model without {b1} everything works, but I would like to
estimate it with and without {b1} (because the interpretation is different).

Thanks in advance.

Miguel.

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