Timeline for Omitted variable bias vs. Multicollinearity
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| May 3, 2020 at 8:03 | vote | accept | Maverick Meerkat | ||
| May 2, 2020 at 10:55 | answer | added | Timothy | timeline score: 2 | |
| May 2, 2020 at 10:46 | comment | added | LSC | Richard is right in the sense that your goal matters but it seems there is confusion about "prediction". Predictions and model performance are generally considered unaffected by multicollinearity. Multicollinearity is a bigger concern when you want to describe the relationships in sample estimated by the beta coefficients or make inferences on the true values/relationships of the betas. | |
| May 2, 2020 at 9:32 | comment | added | Maverick Meerkat | Even though it makes sense, I need to give it some deeper thought. So I won't be accepting it yet. | |
| Mar 15, 2020 at 9:00 | history | tweeted | twitter.com/StackStats/status/1239114242391003137 | ||
| Mar 15, 2020 at 8:27 | answer | added | Richard Hardy | timeline score: 7 | |
| Mar 14, 2020 at 19:13 | comment | added | Richard Hardy | Usually, you would not care about both of them simultaneously. Depending on the goal of your analysis (say, description vs. prediction), you would only care about one of them. For description, multicollinearity is just a fact to be mentioned, just one of the characteristics of the data. For prediction, omitted variable bias is largely irrelevant. | |
| Mar 14, 2020 at 19:07 | history | edited | Nick Cox | CC BY-SA 4.0 |
added 1 character in body; edited title
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| Mar 14, 2020 at 18:54 | history | asked | Maverick Meerkat | CC BY-SA 4.0 |