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I carried out White's test for a particular model to check for heteroskedasticity. I got the following results for White's test (details shown below). AS it can be seen the p-value (0.299) isn't that small so I interpreted it as not having to reject the null hypothesis. However, I also decided to do an rvfplot and I've attached a picture of how it looks as well. To me it doesn't display homoskedasticity. Are my interpretations incorrect?

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Image of the rvfplot:

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    $\begingroup$ The residuals look very nicely distributed to me, I would say there is no heteroskedasticity. $\endgroup$
    – user2974951
    Commented May 26, 2021 at 8:03
  • $\begingroup$ I was a bit uncertain with the diagram but this helped me to clear some of the confusion. Even then if I try to re-estimate the model with heteroskedasticity-robust standard errors (despite the fact it seems unnecessary) and end up getting different values for the standard errors, test statistic and p-values then what would you conclude about the model? Does it have some heteroskedasticity in the end? $\endgroup$
    – Kurapika
    Commented May 26, 2021 at 8:11
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    $\begingroup$ That looks absolutely fine for me, too. $\endgroup$
    – Bernhard
    Commented May 26, 2021 at 8:46
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    $\begingroup$ @Kurapika Where you have fewer data points, the range of your data points is generally also smaller. This is why a fitted vs residuals plot isn't the best for judging heteroscedasticity on its own, but you should also inspect a scale-location plot if you suspect non-constant variance. That said, I agree with the the others that there is nothing remotely problematic judging by the residuals vs fitted plot. $\endgroup$
    – Frans Rodenburg
    Commented May 26, 2021 at 9:53
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    $\begingroup$ Also, note that the robust and conventional standard errors will not be numerically identical in finite samples even if there is no heteroskedasticity. $\endgroup$
    – Christoph Hanck
    Commented May 26, 2021 at 10:03

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