Please support me solve this question: In a simple regression model y = b0 + b1*x + u we have the five main assumptions 1 linearity in parameters 2 random sampling 3 zero conditional mean 4 variation in x 5 homoscedasticity IN ADDITION TO the 5 assumptions, what is the additional assumption for valid hypothesis testing of OLS estimators in the small samples?
In the absence of a followup answer from the OP, I'll post an answer to hopefully save yet another question going unanswered.
1 linearity in parameters 2 random sampling 3 zero conditional mean 4 variation in x
As indicated at the link I mentioned in comments, independence, normality and homoskedasticity of errors are all necessary for the usual normal-theory inference (i.e. confidence intervals, prediction intervals and hypothesis tests).
So that's at least three assumptions you need. Of those, the normality assumption is the easiest one to avoid (e.g. by using inferential procedures that don't assume it), so if I had to nominate only two, I'd have to go with independence and homoskedasticity of errors.