1. Normality assumption Professor Sudarshan Bhattacharjee commented about diagnostic checking as such >> If the data series is sufficiently large... about more than 30 0bservations then you can ignore the violation of normality assumption coz by central limit theorem a long series will converge to normality.... for auto-correlation and heteroskedasticity we can try first some transformation in the variables.... however, if that does not work then use Newey-West test for HC and HAC... This is readily available in R and Stata... Not sure about Eviews.... hope this helps..
2. Normality in the residual Professor Sayed Hossain posted the following figure.
Moritz J. Sch commented> shouldn't we rather state: "we fail to reject the null" instead of "accept the null"?
Sayed Hossain commented> Fail to reject null sounds good
Moritz J. Sch commented> This has somehow become the standard phrase in empirical work. My professor never got tired of mentioning, that "accepting" is a horrible misconception, because the null must not necessarily be true, especially with a p-Value of
Muhammad Anees commented> In research and as per the Williams, Elements of Style, double negatives sounds good :)
George Savva commented> You should not use any statistical test for normality of residuals. Whether they are 'significantly' non-normal is not important for the validity of the regression.