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Re: Re: st: Interaction terms
lreine ycenna <firstname.lastname@example.org>
Re: Re: st: Interaction terms
Wed, 4 May 2011 11:00:19 +0100
I tried the factorial method, however, I have very large
coefficients,high SE, especially when the group dummy G1 is interacted
with all the variables and on sub-sample (e.g. if year < 2000 and >
1995).so my results are mostly insignificant.
(1) I wonder if it's because the second half of my variables are the
bi-products of first half, even though I'm meant to treate these
bi-product variables as individual variables. As I gradually add more
variables, I also have more bi-product variables.
e.g. (a) regress y ov edu wealth gd eduxwealth eduxgd weathxgd ovxedu
ovxwealth ovxgd. ovxeduxwealth ovxeduxgd ovxwealthxgd.
In this case, would it biase my result to include so many bi-product
variables? If so, does it make sense to run all the bi-products
separately on a single regression? e.g. (b) regress y ov eduxwealth
eduxgd weathxgd ovxeduxwealth ovxeduxgd ovxwealthxgd. And then compare
the coefficients in (b) with (a).
(2) I notice that "Regress y ov edu wealth gd ovxedu ovxwealth ovxgd
if G1==1" produces different/ smaller coefficients and SE from that of
the ==G1 command. Would it be incorrect to use the if G1==1 method
instead of regress y i.G1##c.(ov edu wealth gd ovxedu ovxwealth
ovxgd)? I don't quite understand the difference.
(3) what command do I need to divide my data into 5 year panels?
On 3 May 2011 18:11, Christopher Baum <email@example.com> wrote:
> I have two dummies for country groups G1 and G2.
> In the stata command, I could (1) interact the term so it becomes:
> regress y ov edu wealth eduxG2 wealthxG2 ovxeduxG2 ovxwealthxG2
> or (2) I could run: regress y ov edu wealth ovxedu ovxwealth if G1 ==1.
> Would the 2nd method be wrong? What's the difference between (1) and (2)?
> Obviously not the same model. If you wanted to fully interact the G1/G2 split with the model
> regress y ov edu wealth ovxedu ovxwealth
> it would be
> regress y i.G2##(ov edu wealth ovxedu ovxwealth) (1)
> If you then did
> regress y ov edu wealth ovxedu ovxwealth if G1 (2)
> you would get the same point estimates in (1) for the G1 countries as you do in (2), but the standard errors would differ.
> In the pooled regression (1) you are constraining the sigma^2 to be equal across G1 and G2, whereas in (2) and the equivalent
> model you could run for group G2 that constraint is not applied.
> Note that the fully interacted model needs G2 by itself as well as interacted. Your model above with the "xG2" variables does not do that.
> 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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