Thanks Maarten. In this example, OLS and GLM give very similar
econimic effects. In fact, 74 cents for the OLS is really 9.52%
relative to the mean wage of 7.77. This 9.52% is very much in line
with the 9.7% found with GLM. In my case, the coeff. on X for the OLS
is 0.0064. Relative to the mean for the LHS variable of 0.02. This is
an economic effect of about 28%. With the GLS, using exactly your
code, X gets a coefficient of 2.025 or a 102.5% increase in Y. Or
perhaps, I am misinterpreting this coefficient.
Thanks,
Erasmo
On Mon, Feb 15, 2010 at 9:22 AM, Maarten buis <[email protected]> wrote:
> --- On Sun, 14/2/10, Erasmo Giambona wrote:
>> I ran the regressions with both RHS and LHS untransformed
>> using both OLS and GLM with link(log). With the OLS the
>> coeff on X is 0.006 while with the GLM the coefficient is
>> 0.700. I find a bit hard to intepret the GLM coefficient.
>
> Consider the example below:
>
> *--------------- begin example -----------------
> sysuse nlsw88, clear
> gen byte baseline =1
>
> reg wage grade
> glm wage grade baseline, ///
> link(log) eform nocons
> *--------------- end example --------------------
>
>
> The -regress- results are interpreted as follows:
> People without education can expect a wage of
> -1.96 dollars an hour (substantively we know that
> people hardly ever pay for the privelege to work,
> so this is a sign of bad model fit), and they get
> 74 cents an hour more of every additional year of
> education.
>
> The -glm- results are interpreted as follows:
> People without education can expect a wage of
> 2.25 dollars an hour, and for every additional
> year of education they can expect an increase
> of 9.7%.
>
> Hope this helps,
> Maarten
>
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
> http://www.maartenbuis.nl
> --------------------------
>
>
>
>
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