Hello,

I have panel data for 12 industries and 11 years (2006-2017). I want to choose whether I should use fixed effects or first differences - from what I have learnt if we have serially uncorrelated idiosyncratic errors in the original model, FE is better.
However, I was wondering: isn't this time period too small to rely on Durbin-Watson or Breusch-Godfrey test for lag(1)? It is annual data, so I though this could be the case. However, as i want to look for a change in variables over that time period, maybe I should use lag(10) instead?

Thank you.