Hi Statalist,

i am analyzing a dataset of performance for Footballplayers and How a transfer affects Their performance.

To do This i have gathered an overall performance-target 83 players spread on 6 seasons.
Besides that i have a dummy variable which is 1 if the player is sold at the end of the season.

my data would look like This:

season - player - performance - sold
11/12 - Ronaldo - 7,2 - 0
12/13. - Ronaldo - 7,1 - 0
13/14. - Ronaldo - 8,1 - 1 (sold in summer 2014 AFTER the season)
14/15. - Ronaldo - 6,9 - 0
15/16. - Ronaldo - 7,0 - 0
16/17. - Ronaldo - 7,2 - 0

So i want to measure How performance develops leading up to a transfer and afterwards.

to do This i have made a fixed effect model with performance as My Y
and sold as My X. My model now shows me that when sold=1 the performance for that season is higher than normal which is also the effect i would expect.

but now i am Unsure if My coefficients would biased by reverse causality and How i should address This issue?

/Martin