Normality in the residual

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
Normality in the residuals
Hossain Academy Note
Meo School of Research
Shishir Shakya
Noman Arshed
Univariate Models
Multivariate Models