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From |
emanuele mazzini <[email protected]> |

To |
[email protected] |

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 <[email protected]>: > --- 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 > -------------------------- > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: Areg, absorb***From:*emanuele mazzini <[email protected]>

**Re: st: Areg, absorb***From:*Maarten buis <[email protected]>

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