Hello everyone,

I am currently running an OLS panel data regression with industry and time fixed effects and I am testing the assumptions of linear regression. I have a sample with 10.000 datapoints over a time period from 2009-2018. When I conduct my tests however, normality is being violated according to the jarque bera test, which is strange because when I plot my residuals, I can see a distribution that is almost perfect (Normality plot .pdf). Furthermore, when I test for heteroscedasticity and autocorrelation, both tests give me a rather high chi-sq and F score (Tests for autocorrelation and heteroscedasticity.pdf). According to the sayings of several forums and articles, I could solve this by running my regressions with: cluster(FirmID) at the end, and so I did. But when I run my regressions in such a matter, I can no longer test for autocorrelation and heteroscedasticity, so how can I be sure that by clustering the standard errors on the firm ID's the problem of heteroscedasticity and autocorrelation is resolved?

Yours sincereley,

Gyman van der Tol.