Timeline for Why autocorrelation affects OLS coefficient standard errors?
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Sep 9, 2014 at 12:04 | vote | accept | Robert Kubrick | ||
| Sep 7, 2014 at 22:06 | comment | added | Robert Kubrick | @Aniko thank you to confirm! That's what I was trying to understand in my question comments as well. | |
| Sep 7, 2014 at 22:03 | comment | added | Aniko | @RobertKubrick that's exactly the point: having any unmodeled correlation is bad, autocorrelation is just a common special case of having unmodeled correlation. | |
| Sep 7, 2014 at 20:51 | comment | added | KOE | @RobertKubrick, not sure what you mean? Autocorrelation usually refers to $\mathrm{cor}(\epsilon_t,\epsilon_{t-h})\neq 0$ for some $h$. | |
| Sep 7, 2014 at 16:47 | comment | added | Robert Kubrick | @KarlOskar I still don't see anything specific to $Y_{-1}$ in the equation above, only the residuals impact the results (which is true in any case, not just $Y$ autocorrelation). | |
| Sep 7, 2014 at 0:20 | comment | added | KOE | Take the last formula by @StasK and consider the univariate case with nonzero off-diagonal elements (autocorrelation). See what happens in the numerator and compare that to what you get in the numerator when you assume $\Omega=\sigma^2 I$. | |
| Sep 6, 2014 at 23:06 | comment | added | Robert Kubrick | All I get from this is that the higher the residuals, the higher $V[\hat{\beta}]$. So that will increase the SE, not underestimate it. And how is this specific to $Y_{t-1}$? | |
| Sep 6, 2014 at 22:46 | history | answered | StasK | CC BY-SA 3.0 |