Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.
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. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/