I am trying to run an econometric panel data (random effects) model with about 950 observations (so not a small dataset). My data consists of different European public companies and of a few financial statistics of theirs during the last 10 years. However, I am having enormous amount of trouble with the following aspect: According to Jarque-Bera test (p value of 0) I am violating the assumption of normality. While my skewness seems small enough (under 3), the kurtosis has a value of 14. However, I am very puzzled as to what to do now. As one of the options I read that you could take logarithms, but one of my variables (market value added) is often negative and Eviews, obviously, doesn't let me opt for that option. Is there any other solutions to this problem or can anyone offer some advice to an econometric novice? Thank you in advance.
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The cube root is the simplest transformation that pulls in tails and treats values around 0 symmetrically. A reference open to all is Nicholas J. Cox's Stata Journal paper. You don't need to be a Stata user to appreciate the wisdom therein.
when you have n>30, normally people in panel data analyses will be based on assumption of "central Limit theorem". On the other words, we do not have to worry about the formal testing result since that theorem was assumed the data was normally distributed. Thank you.