Search found 6 matches
- Wed Jul 14, 2010 12:08 pm
- Forum: Econometric Discussions
- Topic: Insignificant coefficent, hi R2
- Replies: 9
- Views: 11767
Re: Insignificant coefficent, hi R2
thank you for the answers. this will help a lot for my thesis :)
- Wed Jul 14, 2010 11:49 am
- Forum: Econometric Discussions
- Topic: Insignificant coefficent, hi R2
- Replies: 9
- Views: 11767
Re: Insignificant coefficent, hi R2
i see your point. however it doesnt really apply in my case. im testing mobilprices for some number of variables. in my regression i get collinearity between a markets concentration ratio (measured as hhi) and the intercept. it doesnt apply to any economic reasoning so could it be just a coincidence...
- Wed Jul 14, 2010 9:52 am
- Forum: Econometric Discussions
- Topic: Insignificant coefficent, hi R2
- Replies: 9
- Views: 11767
Re: Insignificant coefficent, hi R2
I would really appreciate some assistance to the question how the intercept term and anotehr coefficent could be collinear
- Tue Jul 13, 2010 10:48 am
- Forum: Econometric Discussions
- Topic: Insignificant coefficent, hi R2
- Replies: 9
- Views: 11767
Re: Insignificant coefficent, hi R2
thanks for your quick answers! your right, it had actually decreased. still though how can i interpret c(1) and another coefficent to be collinear? and that c(1) becomes significant first after removing this coefficent?
- Tue Jul 13, 2010 7:23 am
- Forum: Econometric Discussions
- Topic: Insignificant coefficent, hi R2
- Replies: 9
- Views: 11767
Re: Insignificant coefficent, hi R2
when testing for multicollinearity i used confidence ellipse which implies that c(1) (intercept) and another coefficent is collinear. If i remove the other coefficent c(1) is now significant and R2 is slightly higher. I do not see the logic behind collinearity between the intercept and another coeff...
- Mon Jul 12, 2010 8:23 am
- Forum: Econometric Discussions
- Topic: Insignificant coefficent, hi R2
- Replies: 9
- Views: 11767
Insignificant coefficent, hi R2
I estimated the following linear regression in Eviews: Dependent Variable: PRIS Method: Least Squares Date: 07/12/10 Time: 16:35 Sample: 1 32 Included observations: 32 Variable Coefficient Std. Error t-Statistic Prob. C 11.09973 64.36392 0.172453 0.8647 MARK 0.218873 0.404334 0.541318 0.5940 HHI 0.0...
