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st: AW: re: beta coefficients for interaction terms


From   "Martin Weiss" <martin.weiss1@gmx.de>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: AW: re: beta coefficients for interaction terms
Date   Sun, 21 Jun 2009 16:23:04 +0200

<> 

" so it is not telling you anything for which you need to estimate the  
regression to find out."

So the point estimate is equal to ouput of other commands, bout how about
the CI?


*************
sysuse auto, clear

egen shead = std(headroom)
egen slength = std(length)
gen ia2 = shead*slength
egen sia2 = std(ia2)

/*compare */ 
reg mpg shead slength sia2

mean mpg
*************



HTH
Martin


-----Ursprüngliche Nachricht-----
Von: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Kit Baum
Gesendet: Sonntag, 21. Juni 2009 15:59
An: statalist@hsphsun2.harvard.edu
Betreff: st: re: beta coefficients for interaction terms

<>

Lisa said

but it is important for my paper to show the value of the intercept,
and I get different values if I use either the standardized or the not
standardized dependent variable. If I have the null hypothesis that is
is equal to zero, do I have to use the standardized or not
standardized dependent variable? And coming back to my original
question: Why do I have to standardize the dependent variable?

and in fact if you do standardize both dep and indep vars as suggested  
by an earlier post, the constant term is by definition zero. The  
regression surface passes through the multivariate point of means, and  
that is now (0,0,0,0,0).

When you do run the regression of *un*standardized dep var on  
standardized regressors as you would now like to do, the constant term  
is by construction the *un*conditional mean of the dep var:

. reg mpg shead slength sia2

       Source |       SS       df       MS              Number of obs  
=      74
-------------+------------------------------           F(  3,    70)  
=   41.45
        Model |  1563.44248     3  521.147494           Prob > F       
=  0.0000
     Residual |  880.016979    70  12.5716711           R-squared      
=  0.6398
-------------+------------------------------           Adj R-squared  
=  0.6244
        Total |  2443.45946    73  33.4720474           Root MSE       
=  3.5457

----------------------------------------------------------------------------
--
          mpg |      Coef.   Std. Err.      t    P>|t|     [95% Conf.  
Interval]
------------- 
+----------------------------------------------------------------
        shead |  -.1288064   .4934882    -0.26   0.795     
-1.113038    .8554248
      slength |  -4.598606   .4846015    -9.49   0.000    -5.565113    
-3.632098
         sia2 |   .4815198   .4260752     1.13   0.262    -. 
3682604      1.3313
        _cons |    21.2973   .4121741    51.67   0.000     20.47524     
22.11935
----------------------------------------------------------------------------
--

.
end of do-file

. su mpg

     Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
          mpg |        74     21.2973    5.785503         12         41


so it is not telling you anything for which you need to estimate the  
regression to find out.

Kit Baum   |   Boston College Economics & DIW Berlin   |
http://ideas.repec.org/e/pba1.html
                               An Introduction to Stata Programming   
|   http://www.stata-press.com/books/isp.html
    An Introduction to Modern Econometrics Using Stata  |
http://www.stata-press.com/books/imeus.html



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