Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.
From | Aggie Chidlow <mojamalarybka@googlemail.com> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: collin |
Date | Sat, 12 Mar 2011 16:10:44 +0000 |
Here are the results for sum y* Variable | Obs Mean Std. Dev. Min Max -------------+------------------------------------------------------------------- y_hat | 2251 .3609488 .1824771 4.26e-06 1 On Sat, Mar 12, 2011 at 3:52 PM, DE SOUZA Eric <eric.de_souza@coleurope.eu> wrote: > This is exactly what I thought you had, not just collinearity but perfect collinearity. > The question is: why are you getting perfectly collinearity? > Your y's appear to be constants. > Could you produce the results of -summarize y*- ? > > -----Original Message----- > From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Aggie Chidlow > Sent: 12 March 2011 16:45 > To: statalist@hsphsun2.harvard.edu > Subject: Re: st: collin > > Dear Eric, > Thank you for your advice... will definetly look this reference up. > > When I run my model with all dummies as the reviewer wants me to: > > probit y x1 x2 x3 lnx4 x5 y98 y99 y00 y01 y02 > > where: > y98=463 > y99=494 > y00=425 > y01=406 > y02=376 > y03=88 -not included in the model due to dummies trap > > I get the regression results that say the follwing: > note: y00 omitted because of collinearity > note: y01 omitted because of collinearity > note: y02 omitted because of collinearity > > The coefficients for y00 y01 and y02 are not reported in the model and there is a note which says y00 (omitted); y01 (omitted) and y02 (omitted). > > By the way the collin for year dummies is as follow: > Collinearity Diagnostics > > SQRT R- > Variable VIF VIF Tolerance Squared > ---------------------------------------------------- > y98 -3.37e+13 . -0.0000 1.0000 > y99 -3.53e+13 . -0.0000 1.0000 > y00 -3.16e+13 . -0.0000 1.0000 > y01 -3.05e+13 . -0.0000 1.0000 > y02 -2.87e+13 . -0.0000 1.0000 > y03 -7.74e+12 . -0.0000 1.0000 > ---------------------------------------------------- > Mean VIF -2.79e+13 > > Cond > Eigenval Index > --------------------------------- > 1 2.0000 1.0000 > 2 1.0000 1.4142 > 3 1.0000 1.4142 > 4 1.0000 1.4142 > 5 1.0000 1.4142 > 6 1.0000 1.4142 > 7 0.0000 . > --------------------------------- > Condition Number . > Eigenvalues & Cond Index computed from scaled raw sscp (w/ intercept) > Det(correlation matrix) -0.0000 > > > I would appreciate any suggestions. > > Many thanks in advance. > . > > > On Sat, Mar 12, 2011 at 11:16 AM, DE SOUZA Eric <eric.de_souza@coleurope.eu> wrote: >> I haven't been following this thread till now. >> Jeffrey Wooldridge in his introductory textbook (page 99, international edition) does not encourage use of the VIF . The variance of a coefficient depends on three factors: the standard error of the regression, the total sample variation in the variable attached to the coefficient and the partial R2 . Concentrating on the partial R2 has no justification, even less so the rule of 10. >> >> However, in this case, the referee will probably have to be satisfied in some way or the other. >> >> Aggie, when you say that the dummies were dropped on account of collinearity, what exactly do you mean? >> >> Eric >> >> >> Eric de Souza >> College of Europe >> Brugge (Bruges), Belgium >> http://www.coleurope.eu >> >> >> -----Original Message----- >> From: owner-statalist@hsphsun2.harvard.edu >> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Syed Basher >> Sent: 12 March 2011 11:57 >> To: statalist@hsphsun2.harvard.edu >> Subject: Re: st: collin >> >> Dear Aggie, >> >> I recently used VIF in one of my papers. You can find the discussion here: >> http://ideas.repec.org/p/pra/mprapa/27348.html >> -- See p. 14 (footnote 23) and p. 22 >> >> A general rule of thumb in economics is a VIF>10 indicates harmful collinearity. >> Hope you find this useful. >> >> Syed Basher >> Doha, Qatar. >> >> >> >> >> ----- Original Message ---- >> From: Aggie Chidlow <mojamalarybka@googlemail.com> >> To: statalist@hsphsun2.harvard.edu >> Sent: Sat, March 12, 2011 1:36:26 AM >> Subject: Re: st: collin >> >> Dear Charls and Syed, >> Thank you very much for your comments and suggestions. >> >> I would be thankful very much for your help Syed regarding how to interpret VIF professionaly. Any advice/references would be very much appreciated. >> >> Many thanks,Aggie >> >> On Thu, Mar 10, 2011 at 3:14 PM, Syed Basher <syed.basher@yahoo.com> wrote: >>> Hi Aggie, >>> >>> I think diagnostic checking such as VIF comes before estimation, that >>> is we first check the extent of collinearity among variables using >>> VIF then decide which variables to include in the estimation. After >>> running VIF, you can do >> two >>> sets of estimation: one with all dummies (what the reviewer asked >>> for) and another with least collinear dummies (as you already did), >>> this way the difference between two results will show up. As Charles >>> mentioned, it is >> better >>> to follow what the reviewer has asked for. If you wanted to know how >>> to interpret VIF results professionally, let me know. >>> >>> Syed Basher >>> Doha, Qatar >>> >>> >>> >>> ----- Original Message ---- >>> From: Aggie Chidlow <mojamalarybka@googlemail.com> >>> To: statalist@hsphsun2.harvard.edu >>> Sent: Thu, March 10, 2011 4:30:51 PM >>> Subject: st: collin >>> >>> Dear Stata users, >>> >>> I was appreciate some help regarding "collin" >>> >>> I just got a paper back from a reviewer and he/she wants me to >>> include all my year dummies (i.e. y98 y99 y00 y01 y02 y03) in the >>> following >>> model: probit y x1 x2 x3 lnx4 x5 y98 y99 y00 y01 y02 >>> >>> Previusly in the model I only included two year dummies (i.e y99 and >>> y01) as the others we omitted automatically due to collinearity. >>> I mentioned that in the paper, however, he/she says it is >>> unsatisfactory and I should include them all and than comment on VIF. >>> >>> Please, can somebody tell me how I can go about this? >>> Any advise and/or references will be more than appreciated. >>> >>> Many thanks in advance. >>> Aggie >>> * >>> * 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/ >>> >> >> * >> * 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/ >> >> * >> * 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/ > > * > * 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/