I think the white noise model is always correct. Because you don't know what you don't know, you start with the assumption that everything else is noise, and only start digging more if that hypothesis must be rejected (the residuals don't appear to be normally distributed). Basically, in order to discover whether anything else has non-negligible impact, you need to test whether it is significantly non-random.
But I may be wrong. I am only applying a general modeling method, and in this case there may be a better approach.
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timeasmymeasure
no subject
Date: 2020-02-04 07:23 am (UTC)I think the white noise model is always correct. Because you don't know what you don't know, you start with the assumption that everything else is noise, and only start digging more if that hypothesis must be rejected (the residuals don't appear to be normally distributed). Basically, in order to discover whether anything else has non-negligible impact, you need to test whether it is significantly non-random.
But I may be wrong. I am only applying a general modeling method, and in this case there may be a better approach.