But according to Ulrich Kohler first option is wrong. I included what
he wrote a few months ago.
I think Lisa refer the section on standardized regression cofficients of
that book, particularly to the second item on pg. 201 (English edition).
That item states that one should not use b*s_x/s_y to create
standardized regression coefficients in the presence of interaction
terms. Based on Aiken/West 1991 (28-48) it is recommended that one
should standardize all variables that are part of the interaction in
advance. Hence, instead of coding
. sysuse auto, clear
. gen ia = head*length
. reg mpg head length ia, beta
you should code
. sysuse auto, clear
. egen shead = std(headroom)
. egen slength = std(length)
. egen smpg = std(mpg)
. gen ia2 = shead*slength
. egen sia2 = std(ia2)
. reg smpg shead slength sia2
So I guess the second option gives me the right beta coefficients.
But what do I get from the third option? Are those coefficients
normally included in the regression results?
Best,
Lisa
Zitat von Martin Weiss <[email protected]>:
<>
Well, this thread has produced three options for you to choose from, and you
have heard all the pros and cons. Just to summarize:
*************
sysuse auto, clear
/*first option */
qui gen ia2 = head*length
reg mpg head length ia2, beta
/*second option */
qui{
egen shead = std(headroom)
egen slength = std(length)
egen smpg = std(mpg)
replace ia2 = shead*slength
egen sia2 = std(ia2)
}
reg smpg shead slength sia2
/*third option */
reg mpg shead slength sia2
*************
HTH
Martin
-----Ursprüngliche Nachricht-----
Von: [email protected]
[mailto:[email protected]] Im Auftrag von
[email protected]
Gesendet: Sonntag, 21. Juni 2009 17:33
An: [email protected]
Betreff: Re: st: re: beta coefficients for interaction terms
I am sorry, but I am getting kind of confused here. I am not a pro in
running regressions with interaction terms, I am sorry that I have to
ask so many questions.
To get the beta coefficients I have to run the regression
.reg smpg shead slength sia2
If I run the regression
.reg mpg shead slength sia2
I get the coefficients, that are not standardized?
Am I right?
Best,
Lisa
Zitat von Kit Baum <[email protected]>:
<>
Martin said
" 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?
The unconditional CI is larger:
. means mpg
Variable | Type Obs Mean [95% Conf.
Interval]
-------------+----------------------------------------------------------
mpg | Arithmetic 74 21.2973 19.9569
22.63769
| Geometric 74 20.58444 19.38034
21.86335
| Harmonic 74 19.92318 18.81185
21.17405
------------------------------------------------------------------------
because the standardized regressors that are added to the model are
indeed correlated with mpg:
_cons | 21.2973 .4121741 51.67 0.000 20.47524
22.11935
but as you can see that doesn't make much difference in testing any
meaningful hypothesis about the mean of mpg.
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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