What to do in the eyes of multicollinearity

[Ynwe]

GDP_PER_CAPITA EDUC_PER LAW LIFE_EXPEC REL_PER URB_PER
GDP_PER_CAPITA 1.000000 0.895168 0.956279 0.952616 -0.973835 0.807385
EDUC_PER 0.895168 1.000000 0.975186 0.977476 -0.948947 0.824475
LAW 0.956279 0.975186 1.000000 0.997357 -0.991417 0.853135
LIFE_EXPEC 0.952616 0.977476 0.997357 1.000000 -0.985784 0.867973
REL_PER -0.973835 -0.948947 -0.991417 -0.985784 1.000000 -0.823132
URB_PER 0.807385 0.824475 0.853135 0.867973 -0.823132 1.000000


Sorry that the data isn't super clear, but just by looking at the numbers you should see that I have a huge multicollinearity problem. Even if I reduce the model by the variables that are not significant I still get very high numbers (over 0.8 )

This is actually a big surprise to me. I am looking at very different things such as GDP, a law change, women in tertiary educational facilities, the % of religious people and how urban the society is in my country Austria. This is somewhat similar to some past papers in other countries, so I am surprised to see such high multicolinearity. This is my topic I chose with these variables. I can't add or change my variables anymore at this stage. Should I then just incorporate it into my conclusion and state how my test results, even when significant cannot be taken without a pinch of salt and some other variables will be needed to compensate for this?

edit: is it because I have 3 variables that have a value between 0 and 1 that this could be the root of the problem?

[trubador]

Please do a simple search in the forum before posting your questions:
http://forums.eviews.com/viewtopic.php?f=18&t=7903
http://forums.eviews.com/viewtopic.php?f=4&t=3212
http://forums.eviews.com/viewtopic.php?f=18&t=8752

[Ynwe]

hi trubador sorry shoudl have done that.

I noticed someting while going over a topic

http://forums.eviews.com/viewtopic.php?f=4&t=3212


I used the method described here by vmillias which startz says is actually bad. Why is that so?


So I should use a Coefficient Variance decomposition test right, where you get 2 tables right? And if I understood the test correctly, if the values in the first table (the one higher up) are LOW you have collinearity, while the second table on the bottom shows collinearity if the values are closer to 1, correct? shouldn'T there be a line though, where all values are 1, since every variable explains itself perfectly?

[startz]

Why do you think you should do anything at all about multicollinearity?