1, Scenario:
(1) We are going to run a linear panel data regression;(2) some continuous variables of our model have a high value of skewedness (say, >3) and kurtosis (say, >3).
2, Questions
#1: should we do variable transformation to meet the requirements of variable normality? Some posters suggest that we do normality test after regression.
#2: what is the respective bench mark for skewedness and kurtosis? [-3, +3] for both? Besides, when should we transform a variable: when either its skewedness or kurtosis bypasses the benchmark or both do.
Many thanks!
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