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Re: st: Areg, absorb


From   emanuele mazzini <madsoenistata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Areg, absorb
Date   Mon, 11 Apr 2011 16:15:39 +0200

It does help, of course.
Thank you very much!

2011/4/11 Maarten buis <maartenbuis@yahoo.co.uk>:
> --- On Mon, 11/4/11, emanuele mazzini wrote:
>> do you know a way to not omit the variables that the
>> command xi i.varname generates? I tried with the option
>> noomit, but it seems that it does not work, i.e. it
>> still keeps on omitting the first country of my sample.
>
> Imagine you have two countries Aistan and Bland and that we
> want to predict a variable y. Lets first understand what
> happens when we omit one of the dummies. In this case
> assume we use one dummy variable called bland, which is 1
>  when the country is Bland and 0 when it is not Bland (and
> thus Aistan). In that case we ommited the dummy aistan.
>
> In this case  we have the following equation:
> y_hat = b0 + b1 * bland
>
> If the country is Bland than its predicted values is
> y_hat = b0 + b1 * 1 = b0 + b1
>
> If the country is Aistan than its predicted value is
> y_hat = b0 + b1 * 0 = b0
>
> So the constant is the predicted y for Aistan and b1
> is the difference in predicted y between Aistan and
> Bland.
>
> What will happen when we also include the dummy aistan?
> In this case  we have the following equation:
> y_hat = b0 + b1 * bland + b2 * aistan
>
> If the country is Bland than its predicted values is
> y_hat = b0 + b1 * 1 + b2 * 0 = b0 + b1
>
> If the country is Aistan than its predicted value is
> y_hat = b0 + b1 * 0 + b2 * 1 = b0 + b2
>
> So now there are three parameters to represent two
> predicted values, which means that one of these is
> unidentified. For example we could think that b0 is
> 2, than b1 is the predicted y - 2 for Bland and b2
> is the predicted y - 2 for Aistan. Or we could think
> that b0 is 3, than b1 is the predicted y - 3 for
> Bland and b2 is the predicted y - 3 for Aistan. You
> can see that you can get exactly the same
> predictions for different values of b0, just by
> adjusting the two remaining parameters. There is
> thus no way to distinguish the fit of these
> different models.
>
> In order to be able to estimate the model you must
> constrain one of the parameters. Be default we
> constrain the parameter of one of the dummies to
> be 0 (i.e. we just exclude that variable from our
> model). Alternatively we could constrain the
> constant to be 0, with the -nocons- option.
>
> Anyhow, from your previous question I gathered
> that you are not interested in these effects, you
> even want to suppress the display of these variables.
> In that case I would just stick to the default, all
> these models are mathematically equivalent anyhow.
> But if you are substantively interested in the
> effects of these variables, than this can sometimes
> be a really nice trick that can help the interpretation
> of your model. Notice however, that this does not
> change your model, just the way it is displayed.
>
> 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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