Timeline for Linear regression forecast underestimation
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| May 4, 2012 at 13:56 | comment | added | whuber♦ | The most revealing part is that the fitted values have a mean near 0, showing that @jbowman's surmise is correct. | |
| May 3, 2012 at 23:36 | vote | accept | Robert Kubrick | ||
| May 3, 2012 at 22:14 | answer | added | Rob Hyndman | timeline score: 8 | |
| May 3, 2012 at 21:26 | comment | added | Robert Kubrick | @whuber I added the diagnostics. If I'm not mistaken their impact shouldn't be very large given the number of data points. | |
| May 3, 2012 at 21:25 | history | edited | Robert Kubrick | CC BY-SA 3.0 |
added 331 characters in body
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| May 3, 2012 at 21:14 | comment | added | Robert Kubrick | @jbowman I'm using absolute values because while I was reading through the predictions one at the time I noticed that the relative outcomes were all lower by substantial margin. I added the average of the absolute difference between each prediction and relative outcome to the question. | |
| May 3, 2012 at 20:29 | comment | added | jbowman | 1) Since you're not estimating the absolute values in your model, why are you using absolute values in the check? 2) If the mean of $Y$ is close to 0 (relative to the std. dev. of $Y$), all this comparison will tell you is that the predictions have less dispersion than $Y$, which is a consequence of the estimation procedure and not worth worrying about. | |
| May 3, 2012 at 20:12 | comment | added | whuber♦ | You have some outlying residuals. What do your regression diagnostics say about their leverage and how many of them there are? What do the diagnostic plots suggest about goodness of fit? | |
| May 3, 2012 at 19:59 | history | asked | Robert Kubrick | CC BY-SA 3.0 |