Timeline for Multicollinearity in OLS
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
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| Sep 3, 2023 at 16:42 | comment | added | Peter Flom | Belsley's books are the seminal references. But imagine 10 completely independent variables and one more that is the sum of those 10. You will have perfect collinearity with no strong correlation or relation among any pair. (That's an extreme example, but such things do occur in real life). | |
| Sep 3, 2023 at 14:18 | comment | added | Nip | Please, elaborate more on "Collinearity is a property of sets of independent variables, not just pairs of them". Or share some references. | |
| Sep 22, 2013 at 9:38 | history | edited | Scortchi♦ | CC BY-SA 3.0 |
fixed typo
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| Dec 27, 2012 at 12:54 | comment | added | Peter Flom | I think the really key point is that small changes in the input data can yield big changes in the output. That's a bad thing. Belsley presents one example where changes in the third significant digit of the input reversed the signs of the coefficients. | |
| Dec 27, 2012 at 7:35 | vote | accept | Jase | ||
| Dec 27, 2012 at 2:53 | comment | added | Jase | So what I should take away from Greene's 3 points is that the estimator variance becomes very high, meaning that we are more likely to have estimates that are far away from the population value, and we're likely to find it difficult to get statistical significance? | |
| Dec 26, 2012 at 17:53 | comment | added | Peter Flom | Thanks! I did my dissertation on collinearity - it's been a while (since 1999), but I still remember some stuff :-). | |
| Dec 26, 2012 at 17:51 | comment | added | whuber♦ | +1: That's a good point about how sets of variables can be highly collinear without exhibiting any large correlations. | |
| Dec 26, 2012 at 17:50 | history | answered | Peter Flom | CC BY-SA 3.0 |