I have a similar question:

I am evaluating the impact of options on the payout decision for firms in the period 2012-2016. The panel data set is unbalanced and I'm aware that the data could be incomplete.
Variables N Mean Std. Dev. Min Max
Dependent Variables:
Repurchase Payout 708 0.0021 0.0098 0.0000 0.1899
Dividend Payout 678 0.0323 0.0660 0.0000 0.9686
Independent Variable:
Options 782 0.0044 0.0297 0.0000 0.5684
Control Variables:
Free Cash Flow 778 -0.0215 0.2049 -2.3313 0.4926
Leverage 794 0.2830 0.2352 0.0000 1.9068
Financing Costs 800 21.5723 2.2830 15.2656 28.6068
started by doing the following:
xtset company-id year


I want to know which model I should be using:
  • Conducted a BP test for RE vs OLS (xttest0)
    -> rejected H0 for the dividend variable (xtreg: Dividend= Options + Cash flow + Leverage + Financing costs)
    -> failed to reject H0 for the repurchase variable (xtreg: Repurchase= Options + Cash flow + Leverage + Financing costs)
    ---> Is it possible to use two different models for these regressions when they are based on the same dataset?
  • Additionally, I've visually inspected the residual distribution, to check for heteroskedasticity (as Carlo mentioned in another thread). With the following results:
    Array Array
    --> How do I interpret these outputs (dividend to the left, repurchase to the right)? If there is evidens of heterosked. how do I fix it?
  • What other tests should I run in order to see if the assumptions hold?
And how do I export the test results to word (preferably rtf-format)?

All answers are appreciated
Kind regards,
Ola