Timeline for In linear regression, why are raw least squares residuals heteroskedastic?
Current License: CC BY-SA 4.0
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| Jul 5 at 21:57 | comment | added | kjetil b halvorsen♦ | No, the standard assumption is that the error terms (often denoted $\epsilon_i$) is homoskedastic! | |
| Jun 18, 2019 at 18:41 | history | edited | kjetil b halvorsen♦ | CC BY-SA 4.0 |
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| Jun 17, 2019 at 0:24 | comment | added | Kuku | With regards to the second part of my question, I can understand why it is more convenient to use standardized residuals, but my question I think is more abstract: why the natural heteroskedasticity does not disturb by itself our Gauss-Markov conditions and standard error estimates. As far as I know the formal assumption is not "homoscedasticity of standardized residuals", but only residuals by itself. Aren't we just "covering the sun with a finger"? | |
| Apr 21, 2019 at 19:42 | history | answered | kjetil b halvorsen♦ | CC BY-SA 4.0 |