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From |
lreine ycenna <lreine.ycenna@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: Re: st: Interaction terms |

Date |
Wed, 4 May 2011 11:00:19 +0100 |

Thank you. 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? year Country 1997 UK 1998 UK 1999 UK 2000 UK 2001 UK 2002 UK 2003 UK 2004 UK 2005 UK 2006 UK 2007 UK 2008 UK 1997 AU 1998 AU 1999 AU 2000 AU 2001 AU 2002 AU 2003 AU 2004 AU 2005 AU 2006 AU 2007 AU 2008 AU lreine On 3 May 2011 18:11, Christopher Baum <kit.baum@bc.edu> 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 > > 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 > > > > > * > * 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/

**Follow-Ups**:**Re: Re: st: Interaction terms***From:*maarten buis <maartenlbuis@googlemail.com>

**References**:**re: Re: st: Interaction terms***From:*Christopher Baum <kit.baum@bc.edu>

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