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Nov 6, 2019 at 8:52 vote accept MisterButter
Nov 5, 2019 at 23:39 answer added Henry timeline score: 1
Nov 5, 2019 at 22:15 history edited MisterButter CC BY-SA 4.0
added 55 characters in body
Nov 5, 2019 at 21:35 history reopened whuber
Nov 5, 2019 at 21:29 history edited MisterButter CC BY-SA 4.0
Uppdated question to emit confusion
Nov 5, 2019 at 21:15 review Reopen votes
Nov 5, 2019 at 21:35
Nov 5, 2019 at 21:07 comment added MisterButter Alright, so I should check if the same observation is tied to the same minimum value and correct for this if true? On an other note, when multicollinearity appears in dummy's usually only one dummy is affected, why is both in this case?
Nov 5, 2019 at 20:59 comment added whuber Depending on the nature of the data--that is, assuming few data are tied at the maximum or minimum values--these variables appear to be almost all zeros. That leads to high multicollinearity as well as the possibility of perfect collinearity. You need to check.
Nov 5, 2019 at 20:57 comment added MisterButter @whuber I don't know for sure, but my intuition is that there should be no collinearity since the dummy's are created from different data-series. I can on the otherhand be completly wrong about this.
Nov 5, 2019 at 20:56 comment added MisterButter @gung-ReinstateMonica added the rest of the code, should work now
Nov 5, 2019 at 20:55 history edited MisterButter CC BY-SA 4.0
added code
Nov 5, 2019 at 20:48 comment added whuber Could you show us how you know there is no collinearity? The code shown to construct the *.min4 and *.max4 variables strongly suggests the possibility of perfect collinearity.
Nov 5, 2019 at 20:46 history closed whuber Needs details or clarity
Nov 5, 2019 at 20:41 comment added gung - Reinstate Monica Please add a reproducible example for people to work with.
Nov 5, 2019 at 20:34 history asked MisterButter CC BY-SA 4.0