Timeline for Why autocorrelation affects OLS coefficient standard errors?
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13 events
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| Sep 21, 2021 at 17:18 | answer | added | Suriya Kumar J S | timeline score: 0 | |
| Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
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| Sep 9, 2014 at 12:04 | vote | accept | Robert Kubrick | ||
| Sep 7, 2014 at 17:28 | comment | added | Robert Kubrick | That would be the lagged DV. That is way we observe auto correlated residuals, because $Y$ and $Y_{t-1}$ are correlated. | |
| Sep 7, 2014 at 17:23 | comment | added | whuber♦ | I'm afraid I cannot make any sense of that last comment. It's not even clear what you mean by "$Y_{t-1}$". If by that you mean a lagged variable in a time series, then you are no longer talking about OLS regression. | |
| Sep 7, 2014 at 15:35 | comment | added | Robert Kubrick | ok, but how is this different than the case where we don't have any residuals autocorrelation, but we're not including another critical predictor? We can draw the same confidence conclusions because of that other critical predictor. The only difference is that in the case of a missing $Y_{t-1}$ critical predictor we can observe autocorrelation in the residuals. Other than that, I'm not clear what makes autocorrelation stand apart from other critical predictors. | |
| Sep 7, 2014 at 13:34 | comment | added | whuber♦ | My example is not missing any predictors at all: it is only positing an extreme case of autocorrelation among the residuals. | |
| Sep 7, 2014 at 5:44 | history | tweeted | twitter.com/#!/StackStats/status/508491088307703808 | ||
| Sep 6, 2014 at 22:59 | comment | added | Robert Kubrick | Yes, but that is true of any missing predictor that could explain 99% of the variance and we just ignore. Why are making a specific case for $Y_{t-1}$? | |
| Sep 6, 2014 at 22:55 | comment | added | whuber♦ | Consider an extreme case of correlation. Suppose all the errors were perfectly positively correlated. In other words, somebody had generated a single random number and added it to all the response values. How certain would you be of (say) the intercept in the regression? Would you have any clues at all concerning the size of the random value that was added? | |
| Sep 6, 2014 at 22:46 | answer | added | StasK | timeline score: 10 | |
| Sep 6, 2014 at 22:45 | history | edited | Glen_b | CC BY-SA 3.0 |
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| Sep 6, 2014 at 22:34 | history | asked | Robert Kubrick | CC BY-SA 3.0 |